aditya-srikanth/Clustering-Systems

This is our implementation of the second assignment of our Data Mining course.

Clustering Systems

This repository contains implementations of K-Means Clustering and Hierarchical Clustering.

K-Means

Hierarchical Clustering

The current hierarchical clustering algorithms uses agglomeration to create the clusters and uses the following linkages:

1. Single Link (MIN)
2. Complete Link (MAX)
3. Group Average (AVG)

The agglomerative hierarchical clustering script is divided into two classes:

1. Agglomerative_Hierarchical
2. Proximity_Matrix

These are further divided as:

1. matrix_min(): Returns the current minimum value in the passed matrix.
2. min_cluster_distance(): Returns the minimum distance between clusters.
3. max_cluster_distance(): Returns the maximum distance between clusters.
4. avg_cluster_distance(): Returns the average distance between clusters.
5. matrix_gen(): Generates a new proximity matrix after cluster formation.
6. clustering(): Clusters points agglomeratively and returns the linkage matrix.
7. distance(): Calculates distance between points.
8. raw_matrix(): Generates the proximity matrix for the first time from data.

Results

The algorithms were run on a dataset consisting of amino acid sequences. The results are published as dendrograms:

K-Means Clustering

Hierarchical clustering:

1. Single Link

2. Complete Link

3. Group Average

Libraries Used

1. Numpy
2. Scipy
3. Matplotlib

Authors

![Aditya Srikanth] ![Prateek Das Gupta]