knowledgediscoveryindata.ipynb

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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/UdayLab/PAMI/blob/main/knowledgediscoveryindata.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Walk through: Knowledge Discovery in Big Data\n",
        "\n",
        "\n",
        "> Illustration with 5-year nation-wide air pollution data of Japan\n",
        "\n"
      ],
      "metadata": {
        "id": "9kxtMcdavLld"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Introduction\n",
        "\n",
        "Knowledge discovery in data in data has the following five steps:\n",
        "\n",
        "\n",
        "\n",
        "1.   Selection : selecting the right data from the whole data available\n",
        "2.   Preprocessing : Modifying the data to remove abnormalities and bring consistency\n",
        "3.   Transform : Changing the data format so that the machine learning algorithm can accept it.\n",
        "4.   Data mining : Applying the knowledge discovery algorithms\n",
        "5.   Interpretation/Evalution : Analyze the discovered knowledge.\n",
        "\n",
        "\n",
        "In the below figure, left-side presents the KDD process and the right-side illustrates the example. Using the air pollution data, we illustrate the KDD process."
      ],
      "metadata": {
        "id": "V_0ZTN7ExFVq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1Et62teoZoTYQgxGCw3qRiJniJLD1G3c6)"
      ],
      "metadata": {
        "id": "XdhA6JgGb43J"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {
        "id": "n23weIHrkvqF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Step 0: Data\n"
      ],
      "metadata": {
        "id": "zABbPAWlzzf-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1E3UZfjL8IDqtOTmECm3sTm5fHPr57GoC)"
      ],
      "metadata": {
        "id": "ARIr1I7AO73V"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "## About the data\n",
        "Air pollution is a significant cause of the cardio-respiratory problems reported in Japan. On average, forty thousand people die annually in Japan due to pollution [(click here)](https://www.statista.com/statistics/935022/number-deaths-air-pollution-japan/#:~:text=In%202019%2C%20the%20number%20of,attributable%20to%20air%20pollution%20exposure.). To confront this problem, The Ministry of Environment, Japan, has set up a sensor network system, [SORAMAME](https://soramame.env.go.jp/), to monitor pollution in Japan. Each sensor in this network collects pollution levels of various air pollutants (e.g., SO2, NO2, and PM2.5) at hourly intervals.\n",
        "\n",
        "In this experiment, we will be using the 5+ years of PM 2.5 pollutants data generated by all sensors situated throughout Japan."
      ],
      "metadata": {
        "id": "M4Jz_rYicFWp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 0.1: Download the air pollution dataset"
      ],
      "metadata": {
        "id": "yJnl4flmRxJV"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "P6B2vwnWu6oa",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "128e1b30-e09f-4fd7-d80a-6c87f36746a6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-11-07 04:51:05--  https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv\n",
            "Resolving www.dropbox.com (www.dropbox.com)... 162.125.1.18, 2620:100:6016:18::a27d:112\n",
            "Connecting to www.dropbox.com (www.dropbox.com)|162.125.1.18|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://www.dropbox.com/scl/fi/xfqhfaqam1mbvmlapv5z1/ETL_DATA_new.csv?rlkey=fcox2uze73hrreid6e78nvufg [following]\n",
            "--2024-11-07 04:51:05--  https://www.dropbox.com/scl/fi/xfqhfaqam1mbvmlapv5z1/ETL_DATA_new.csv?rlkey=fcox2uze73hrreid6e78nvufg\n",
            "Reusing existing connection to www.dropbox.com:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://uc482c095e1e1908ef09f2257f19.dl.dropboxusercontent.com/cd/0/inline/Cd563zyRjMpkLAyKkpas5QUZ0oJo9YvK3BO0ds2eWtZ4uqSeiAVCD5Y5Zp9Z4WWVNbY8lyN1yrLEM1qH-UvEFs9zcIgbmPv4zektNl_s8ZhccKwwSwS-3PxzOgVgqAYtqei9BYwp2q-3BwWU5MYQruPX/file# [following]\n",
            "--2024-11-07 04:51:05--  https://uc482c095e1e1908ef09f2257f19.dl.dropboxusercontent.com/cd/0/inline/Cd563zyRjMpkLAyKkpas5QUZ0oJo9YvK3BO0ds2eWtZ4uqSeiAVCD5Y5Zp9Z4WWVNbY8lyN1yrLEM1qH-UvEFs9zcIgbmPv4zektNl_s8ZhccKwwSwS-3PxzOgVgqAYtqei9BYwp2q-3BwWU5MYQruPX/file\n",
            "Resolving uc482c095e1e1908ef09f2257f19.dl.dropboxusercontent.com (uc482c095e1e1908ef09f2257f19.dl.dropboxusercontent.com)... 162.125.1.15, 2620:100:6016:15::a27d:10f\n",
            "Connecting to uc482c095e1e1908ef09f2257f19.dl.dropboxusercontent.com (uc482c095e1e1908ef09f2257f19.dl.dropboxusercontent.com)|162.125.1.15|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 262918270 (251M) [text/plain]\n",
            "Saving to: ‘ETL_DATA_new.csv’\n",
            "\n",
            "ETL_DATA_new.csv    100%[===================>] 250.74M  67.8MB/s    in 3.8s    \n",
            "\n",
            "2024-11-07 04:51:10 (66.8 MB/s) - ‘ETL_DATA_new.csv’ saved [262918270/262918270]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://www.dropbox.com/s/wa8d1sujzlx56hh/ETL_DATA_new.csv"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 0.2: Read the dataset and view it"
      ],
      "metadata": {
        "id": "DYJOJKOq3Qr2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "dataset = pd.read_csv('ETL_DATA_new.csv', index_col=0)\n",
        "\n",
        "dataset\n",
        "# you can notice that dataset is collected from 2018-01-01 01:00:00 hours to 2023-04-25 22:00:00 hours (5+ years)"
      ],
      "metadata": {
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          "height": 649
        },
        "id": "vsonMLxu1CMG",
        "outputId": "581f5349-6977-4fec-cebb-7c568f10b7af"
      },
      "execution_count": null,
      "outputs": [
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              "    <tr>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>46000</th>\n",
              "      <td>2023-04-25 18:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>22.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46001</th>\n",
              "      <td>2023-04-25 19:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>21.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46002</th>\n",
              "      <td>2023-04-25 20:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>20.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>19.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46003</th>\n",
              "      <td>2023-04-25 21:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46004</th>\n",
              "      <td>2023-04-25 22:00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>9.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>46005 rows × 1832 columns</p>\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
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              "      border-color: transparent;\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
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              "\n",
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              "\n",
              "      .colab-df-generate:hover {\n",
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              "\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Observations:\n",
        "\n",
        "> 1. __Format of the dataset:__\n",
        "\n",
        "     timestamp _tabSpace_ sensorLocation1 _tabSpace_ sensorLocation2 _tabSpace_ ... _tabSpace_ sensorLocationN\n",
        "\n",
        "\n",
        "> 2. If a sensor does not have a location information, it will have the title \"unnamed\"\n",
        "\n",
        "> 3. \"NaN\" value in a cell provides the information that the corresponding sensor has not recorded the value of an air pollutant at a particular timestamp\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "OYkNAVld306s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {
        "id": "RfH9aAqtkyxx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 1: Selection\n",
        "\n"
      ],
      "metadata": {
        "id": "fT3bqf_P4aMp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1E0aMqWVomUK54cI3MF8AXS9i-ZXwCp_W)"
      ],
      "metadata": {
        "id": "NeK1ih46RKmY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Step 1.1: Removing timestamp information\n",
        "\n",
        "Since the index of the data implicitly records the timestamp information, we _select_ the data without timestamp. In other words, our target data is the data without timestamp information."
      ],
      "metadata": {
        "id": "b2kRGz35pBFg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.drop('TimeStamp', inplace=True, axis=1)\n",
        "\n",
        "dataset"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 475
        },
        "id": "Rl9D_VTZ30i_",
        "outputId": "3350741f-0a30-430e-883b-9569eda758d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Point(139.0794379 36.3727776)  Point(139.1051411 36.3963822)  \\\n",
              "                                                                      \n",
              "0                                NaN                            NaN   \n",
              "1                                NaN                            NaN   \n",
              "2                                NaN                            NaN   \n",
              "3                                NaN                            NaN   \n",
              "4                                NaN                            NaN   \n",
              "...                              ...                            ...   \n",
              "46000                            NaN                            NaN   \n",
              "46001                            NaN                            NaN   \n",
              "46002                            NaN                            NaN   \n",
              "46003                            NaN                            NaN   \n",
              "46004                            NaN                            NaN   \n",
              "\n",
              "       Point(139.0960211 36.4047323)  Point(139.0428727 36.3816035)  \\\n",
              "                                                                      \n",
              "0                                5.0                           13.0   \n",
              "1                               11.0                           12.0   \n",
              "2                                7.0                           12.0   \n",
              "3                                5.0                           11.0   \n",
              "4                                6.0                           11.0   \n",
              "...                              ...                            ...   \n",
              "46000                            NaN                           22.0   \n",
              "46001                            NaN                           21.0   \n",
              "46002                            NaN                           20.0   \n",
              "46003                            NaN                           19.0   \n",
              "46004                            NaN                           19.0   \n",
              "\n",
              "       Point(138.9955116 36.33801589999999)  Point(139.342672 36.4105658)  \\\n",
              "                                                                            \n",
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          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "#### Step 1.2: Removing Sensors that do not have Point information\n",
        "\n",
        "If a sensor does not have location information, we do not know from whether the data was collected. Henceforth, we remove the data of the sensors that do not have location information."
      ],
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        "sensorsWithPointInformation = [col for col in dataset if 'Unnamed' in col]\n",
        "dataset.drop(columns=sensorsWithPointInformation, inplace=True, axis=1)\n",
        "dataset.head() # This is our target data for analysis"
      ],
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        "id": "2JIJo0LIoi_2"
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      "execution_count": null,
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Observation:** Out of 1831 sensors (or columns) in Step 1.1, only 1764 sensors (or columns) were considered for the analysis.\n",
        "\n",
        "This is our target data. We will be performing analytics on this data and discover interesting knowledge."
      ],
      "metadata": {
        "id": "mxvl2wGFiTy7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {
        "id": "BwALgAX6k2Hy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 2: Preprocessing\n",
        "\n",
        "In this step, we will be handling the abnormalities in the data.\n"
      ],
      "metadata": {
        "id": "Ym9fqZJO6FAH"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1E6GIr62Wxi-JVTOs1VI7zKuLoVH1LbzA)"
      ],
      "metadata": {
        "id": "zazT5AYIRZZm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "#### Step 2.1: Fillup the missing values (NaN) with zero"
      ],
      "metadata": {
        "id": "BBykHV1_lEJM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset = dataset.fillna(0)\n",
        "dataset.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 287
        },
        "id": "Qka2TNy86OCx",
        "outputId": "07b2c1bf-e583-4699-ba43-18b7d6d70d1c"
      },
      "execution_count": null,
      "outputs": [
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              "      <td>6.0</td>\n",
              "      <td>11.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>27.0</td>\n",
              "      <td>...</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "<p>5 rows × 1764 columns</p>\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "dataset"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Step 2.2: Removing abnormal values in the data\n",
        "\n",
        "PM2.5 values of a sensor in general vary from 0 to 250. Henceforth, if we find values that do not lie between 0 and 250, we prone those values by setting the value of zero.\n",
        "\n",
        "(PM2.5 values can be above 250 also; however, such values are hard to find in Japan. So we set 250 as the upper bound.)"
      ],
      "metadata": {
        "id": "MJeJRYFfZRfW"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Step 2.2.1: Finding the maximum values recorded by each sensor"
      ],
      "metadata": {
        "id": "oFNpo4dJa3u7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "maxValueInEachColumn = dataset.max() #Gathering the maximum PM2.5 value recorded by each sensor\n",
        "maxValueInEachColumn      #Printing the maxValue of each sensor"
      ],
      "metadata": {
        "id": "FT2EIM1ZVzRf",
        "outputId": "25e3a22b-e87f-4ce2-e700-60a2d49259db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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              "Point(139.1051411 36.3963822)             0.0\n",
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              "Point(139.7151723 36.822353)              0.0\n",
              "Point(140.1510903 36.6598314)           165.0\n",
              "Length: 1764, dtype: float64"
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            "text/html": [
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              "      <td>110.0</td>\n",
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              "      <td>200.0</td>\n",
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              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "<p>1764 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maxValueInEachColumn.plot()  #point the maximum values recorded by each sensor."
      ],
      "metadata": {
        "id": "Wmvv1z6mZwA2",
        "outputId": "f0aeb03f-2461-46cd-90f4-1c39dec9e9af",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "__Observation:__ we can see many sensors have recorded high PM2.5 values greater than 250. Such values are generally outliers/abnormalities and are not useful for the analysis."
      ],
      "metadata": {
        "id": "tBrwAabsaJmE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Step 2.2.2: Replacing the values greater than 250 to zero."
      ],
      "metadata": {
        "id": "6ZAm2FtlaxQT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.where(dataset <= 250, 0, inplace=True)\n",
        "dataset.max().plot()"
      ],
      "metadata": {
        "id": "7Vx27DZ_bG8L",
        "outputId": "82a5b613-808b-47cd-d8b9-9ac66646fad6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "__Observation:__ We can notice that the maximum values of every sensor are no more than the 250 value.\n",
        "\n",
        "We will now check for the minimum values recorded by the sensors."
      ],
      "metadata": {
        "id": "opYvY-uQb1fR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Step 2.2.3: Finding the minimum values recorded by each sensor"
      ],
      "metadata": {
        "id": "JGSmOe8EcI8l"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "minValueInEachColumn = dataset.min() #Reading the minimum PM2.5 value recorded by each sensor\n",
        "minValueInEachColumn.plot()  #ploting them\n",
        "\n",
        "#dataset.min().plot()      #memory efficient approach"
      ],
      "metadata": {
        "id": "zwbbXYqCZEo2",
        "outputId": "798fae67-3c18-4790-ca73-f75591cc9134",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "__Observation:__ We can observe that many sensors have recorded negative PM2.5 values. Thus, we replace the negative PM2.5 values of each sensor with Zero"
      ],
      "metadata": {
        "id": "buCv95fkc2sO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Step 2.2.4: Replacing the values less than zero to 0"
      ],
      "metadata": {
        "id": "OYlUYzY1dTjW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.where(dataset > 0, 0, inplace=True)\n",
        "dataset.min().plot()"
      ],
      "metadata": {
        "id": "Q0DaB2g_daf8",
        "outputId": "c9e8d43d-8e08-4f3c-d9d0-ff6755174de8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "__Observation:__ The abnormal values were replaced to 0."
      ],
      "metadata": {
        "id": "U1O0xBeJd8Sl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "### Step 3: Data transformation\n",
        "\n",
        "In this step, we will transform the processed data into a binary transactional database so that we can discover frequent patterns (or areas) that have recorded high levels of pollution."
      ],
      "metadata": {
        "id": "wlRyCAXzlJsU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1E0CqIDShlbjBq8s2sVcMbx9w-LzQht-Y)"
      ],
      "metadata": {
        "id": "FagQc3juSVu1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Step 3.0: We will install the latest version of PAMI package to perform\n",
        "\n",
        "- data transformation from data frame to transactional database\n",
        "\n",
        "- frequent pattern mining"
      ],
      "metadata": {
        "id": "eJ_MqM0dpnmM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U pami"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Im02B5LdO6cJ",
        "outputId": "c0f1350c-77af-42a6-ac94-06b2751c0329"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting pami\n",
            "  Downloading pami-2024.10.24.2-py3-none-any.whl.metadata (80 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/80.3 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m80.3/80.3 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from pami) (5.9.5)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from pami) (2.2.2)\n",
            "Requirement already satisfied: plotly in /usr/local/lib/python3.10/dist-packages (from pami) (5.24.1)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.10/dist-packages (from pami) (3.8.0)\n",
            "Collecting resource (from pami)\n",
            "  Downloading Resource-0.2.1-py2.py3-none-any.whl.metadata (478 bytes)\n",
            "Collecting validators (from pami)\n",
            "  Downloading validators-0.34.0-py3-none-any.whl.metadata (3.8 kB)\n",
            "Requirement already satisfied: urllib3 in /usr/local/lib/python3.10/dist-packages (from pami) (2.2.3)\n",
            "Requirement already satisfied: Pillow in /usr/local/lib/python3.10/dist-packages (from pami) (10.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from pami) (1.26.4)\n",
            "Requirement already satisfied: sphinx in /usr/local/lib/python3.10/dist-packages (from pami) (5.0.2)\n",
            "Collecting sphinx-rtd-theme (from pami)\n",
            "  Downloading sphinx_rtd_theme-3.0.1-py2.py3-none-any.whl.metadata (4.4 kB)\n",
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            "Successfully installed JsonForm-0.0.2 JsonSir-0.0.2 discord.py-2.4.0 docutils-0.21.2 pami-2024.10.24.2 python-easyconfig-0.1.7 resource-0.2.1 sphinx-8.1.3 sphinx-rtd-theme-3.0.1 sphinxcontrib-jquery-4.1 validators-0.34.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Step 3.1: Converting the data frame into a transactional database\n",
        "\n",
        "As per the Japanese standards, the PM2.5 values greater than or equal to 35 are considered \"unhealthy\" for the people. Henceforth, we create a transactional database by considering the sensor values greater than or equal to 35."
      ],
      "metadata": {
        "id": "FsT19X0fp1ux"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#TIME TAKING PROCESS DEPENDING ON THE DATA.\n",
        "\n",
        "from PAMI.extras.convert import denseDF2DB as db\n",
        "\n",
        "obj = db.denseDF2DB(dataset)\n",
        "obj.convert2TransactionalDatabase('PM25HeavyPollutionRecordingSensors.csv', '>=', 35)"
      ],
      "metadata": {
        "id": "smUkVkF4O3By"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Step 3.2: Printing few lines of the transactional database"
      ],
      "metadata": {
        "id": "d_Git4hAmtr-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!head PM25HeavyPollutionRecordingSensors.csv"
      ],
      "metadata": {
        "id": "1WLKIXq9msqW",
        "outputId": "f5a8df8b-db8a-4e49-91ac-656cd1816b99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Point(139.750635 35.7977241)\tPoint(139.8841693 35.8422674)\tPoint(139.3819898 36.2229999)\tPoint(139.9206727 35.684037)\tPoint(139.9785953 35.6880639)\tPoint(139.9033705 35.7876179)\tPoint(139.9123927 35.7995549)\tPoint(139.880097 35.953403)\tPoint(139.9035901 35.8570293)\tPoint(139.9012134 35.6552406)\tPoint(139.8356927 35.6967785)\tPoint(139.7209595 35.6108138)\tPoint(139.7054233 35.7609043)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8773135 35.6821907)\tPoint(137.1418898 34.9626945)\tPoint(136.8905668 35.0122987)\tPoint(136.6548337 35.0051925)\tPoint(139.4949175 36.2914457)\n",
            "Point(139.7962265 35.8959041)\tPoint(139.8841693 35.8422674)\tPoint(139.3819898 36.2229999)\tPoint(139.9206727 35.684037)\tPoint(139.9785953 35.6880639)\tPoint(139.9033705 35.7876179)\tPoint(139.9123927 35.7995549)\tPoint(139.880097 35.953403)\tPoint(139.9012134 35.6552406)\tPoint(139.8276492 35.689975)\tPoint(139.8356927 35.6967785)\tPoint(139.7054233 35.7609043)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8776074 35.744251)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8859668 35.7099429)\tPoint(139.8773135 35.6821907)\tPoint(139.5257126 35.3986601)\tPoint(139.4898296 35.425629)\tPoint(139.6704215 35.2829123)\tPoint(138.3366179 34.9856239)\tPoint(136.8880309 35.1033076)\tPoint(137.1882268 34.9467181)\tPoint(137.1418898 34.9626945)\tPoint(136.9121266 35.0427414)\tPoint(130.1948336 32.4611967)\n",
            "Point(139.8841693 35.8422674)\tPoint(140.067069 35.6445893)\tPoint(139.9785953 35.6880639)\tPoint(139.9033705 35.7876179)\tPoint(139.9123927 35.7995549)\tPoint(140.0679921 35.5260141)\tPoint(139.9012134 35.6552406)\tPoint(139.8276492 35.689975)\tPoint(139.8356927 35.6967785)\tPoint(139.7054233 35.7609043)\tPoint(139.5990181 35.7377286)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8776074 35.744251)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8859668 35.7099429)\tPoint(139.8773135 35.6821907)\tPoint(139.871021 35.6536708)\tPoint(130.1948336 32.4611967)\tPoint(140.5167529 36.1595964)\n",
            "Point(139.8841693 35.8422674)\tPoint(139.9033705 35.7876179)\tPoint(139.9012134 35.6552406)\tPoint(139.8356927 35.6967785)\tPoint(139.7054233 35.7609043)\tPoint(139.8257782 35.7697167)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8776074 35.744251)\tPoint(139.8692678 35.7864558)\tPoint(139.8530976 35.7608755)\tPoint(139.8859668 35.7099429)\tPoint(139.8773135 35.6821907)\tPoint(139.871021 35.6536708)\n",
            "Point(139.9033705 35.7876179)\tPoint(139.880097 35.953403)\tPoint(139.9012134 35.6552406)\tPoint(139.8356927 35.6967785)\tPoint(139.8045157 35.77453510000001)\tPoint(139.8692678 35.7864558)\tPoint(139.871021 35.6536708)\tPoint(139.6849913 35.5003919)\n",
            "Point(140.1909326 36.0707298)\n",
            "Point(139.5569825 36.1754206)\tPoint(139.9635447 35.9015724)\tPoint(139.9602831 36.1842223)\tPoint(139.9920425 36.0342626)\n",
            "Point(139.3819898 36.2229999)\tPoint(140.002113 35.8406241)\tPoint(140.1909326 36.0707298)\tPoint(139.9920425 36.0342626)\tPoint(140.0495324 35.9110671)\tPoint(140.31192 35.962133)\n",
            "Point(140.2064792 35.7277762)\tPoint(140.5779348 35.7212275)\tPoint(140.1241332 35.5356071)\tPoint(140.0679921 35.5260141)\tPoint(133.3943468 34.4692547)\n",
            "Point(141.6892571 42.6698527)\tPoint(139.9033705 35.7876179)\tPoint(140.0679921 35.5260141)\tPoint(139.4027683 35.3336185)\tPoint(135.4092159 34.8133456)\tPoint(133.3943468 34.4692547)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Each line in this file represents the sensors that have recorded high levels of PM2.5 values at an hour."
      ],
      "metadata": {
        "id": "r8Kz4yKAnWBl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 4: Data Mining\n",
        "\n",
        "Frequent pattern mining is a popular knowledge discovery technique in data mining. It aims to find those interesting hidden patterns that have appeared frequently in the data.\n",
        "\n",
        "In this experiment, we will perform frequent pattern mining on the previously constructed PM2.5 transactioinal database and identify the locations that have recorded high levels of pollution."
      ],
      "metadata": {
        "id": "rkppthohO2yJ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1E3TxWjv-2uaFxoGTTRAyj6qYlelgDpIB)"
      ],
      "metadata": {
        "id": "4Tt01MqZTH82"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Step 4.1: Understanding the distribution (or statistics) of data\n",
        "\n",
        "Distribution of data plays a vital role in the success of a data mining task. Henceforth, let us analyze the distribution of the data."
      ],
      "metadata": {
        "id": "tRf8orOpoWV3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.extras.dbStats import TransactionalDatabase as tds\n",
        "obj = tds.TransactionalDatabase('PM25HeavyPollutionRecordingSensors.csv')\n",
        "obj.run()\n",
        "obj.printStats()\n",
        "obj.plotGraphs()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xm6W5dCBPPbo",
        "outputId": "1832c257-8d6f-488d-eb58-5e18d7421bd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Database size (total no of transactions) : 31462\n",
            "Number of items : 1119\n",
            "Minimum Transaction Size : 1\n",
            "Average Transaction Size : 16.545896637213147\n",
            "Maximum Transaction Size : 503\n",
            "Standard Deviation Transaction Size : 39.73276427461552\n",
            "Variance in Transaction Sizes : 1578.7427362530073\n",
            "Sparsity : 0.9852136759274235\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Step 4.2: Mining Frequent Patterns using FP-growth algorithm"
      ],
      "metadata": {
        "id": "3Xw24J5sEP3n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.frequentPattern.basic import FPGrowth as ab\n",
        "obj = ab.FPGrowth('PM25HeavyPollutionRecordingSensors.csv', 500) #If the execution is taking too much time, kill the process and use a higher minimum support value.\n",
        "obj.mine()\n",
        "obj.printResults()\n",
        "obj.save('soramameFrequentPatterns_1000.txt') #It is a good practise to store the parameters used for an experiment as a file name"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oGprbukCQFXY",
        "outputId": "53053a07-b8ea-4550-ef7a-4f1fcd900447"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Frequent patterns were generated successfully using frequentPatternGrowth algorithm\n",
            "Total number of Frequent Patterns: 1194\n",
            "Total Memory in USS: 2302046208\n",
            "Total Memory in RSS 2324738048\n",
            "Total ExecutionTime in ms: 11.09497857093811\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "***"
      ],
      "metadata": {
        "id": "z3RKW9cDTVe1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Step 5: Interpretation and Evaluation.\n",
        "\n",
        "In this step, we will spatially visualize top-10 long patterns and make necessary interpretations."
      ],
      "metadata": {
        "id": "A9X6ymGdQI08"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![](https://drive.google.com/uc?export=view&id=1DwRdNbPphDKLGjoZy2bs2JoR53bjfGFY)"
      ],
      "metadata": {
        "id": "T8nxee6oTXa3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from PAMI.extras.graph import visualizePatterns as fig\n",
        "\n",
        "obj = fig.visualizePatterns('soramameFrequentPatterns_1000.txt',10)\n",
        "obj.visualize(width=1000,height=900)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "XCX7QBEuQITs",
        "outputId": "cf1d5a9f-8d87-457e-e076-43c8ae28e602"
      },
      "execution_count": null,
      "outputs": [
        {
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          "name": "stdout",
          "text": [
            "Number \t Pattern\n",
            "2\tPoint(139.6623882 36.0656055)\tPoint(140.5781487 39.3100292)\tPoint(139.6681689 36.0701176)\n",
            "3\tPoint(139.6623882 36.0656055)\tPoint(139.9717795 35.8439578)\tPoint(139.6681689 36.0701176)\n",
            "4\tPoint(139.6681689 36.0701176)\tPoint(130.6999328 33.0093764)\tPoint(130.6384926 32.5010333)\n",
            "5\tPoint(130.8163704 32.7914033)\tPoint(130.6384926 32.5010333)\tPoint(130.6999328 33.0093764)\n",
            "6\tPoint(130.6999328 33.0093764)\tPoint(140.5781487 39.3100292)\tPoint(130.6384926 32.5010333)\n",
            "7\tPoint(130.7998865 33.868205)\tPoint(130.6939898 33.89368080000001)\tPoint(130.8073796 33.9015062)\n",
            "8\tPoint(139.0432674 36.64710669999999)\tPoint(139.6623882 36.0656055)\tPoint(139.6681689 36.0701176)\n",
            "9\tPoint(130.6939898 33.89368080000001)\tPoint(130.8293822 33.8952617)\tPoint(130.8073796 33.9015062)\n",
            "10\tPoint(130.7998865 33.868205)\tPoint(130.8293822 33.8952617)\tPoint(130.850335 33.8863949)\tPoint(130.8073796 33.9015062)\n",
            "11\tPoint(133.7758944 34.6007931)\tPoint(133.8362679 34.5899827)\tPoint(133.7570366 34.5800143)\tPoint(133.8171478 34.6075268)\n"
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        "**Knowledge**\n",
        "\n",
        "The following interpretations can drawn from these figures:\n",
        "\n",
        "1. Costal areas are observing high levels of pollution\n",
        "\n",
        "2. Pollution was observed highly at Kyushu and Tokyo."
      ],
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      "source": [
        "**References:**\n",
        "\n",
        "\n",
        "1.   RAGE, Uday kiran (2023), “5+ Years of Multiple Time Series Data of Hourly PM2.5 Recordings Gathered from Various Sensors Located throughout Japan (1-1-2018 to 25-4-2023)”, Mendeley Data, V1, doi: 10.17632/phgrnvykmr.1\n",
        "2.   PAMI: PAttern Mining toolkit. https://github.com/UdayLab/PAMI\n",
        "\n"
      ],
      "metadata": {
        "id": "NqQ1qbZY2120"
      }
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