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Data-Mining-Algorithms

This library has support for various data clustering algorithms as well as frequent-subgraph-mining on graph datasets.

Data Clustering

We provide support for the following algorithms on datasets ranging from 1 - 5 features dimensions.

  • K-Means
  • DBScan
  • OPTICS

We credit nanoflann for its quick implementation in finding nearest-neighbors in KD trees.

Frequent-Subgraph-Mining

We support the implementation of the various subgraph-mining algorithm on graph datasets. We provide an example on the classification of active molecules in chemical compounds w.r.t. a particular disease.
The algorithms we implement are:

  • FSG
  • GSpan
  • Gaston

Authors

Course Project under Prof. Sayan Ranu