Clustering-Methods

COMP 4331 Knowledge Discovery and Data

Mining, Fall 2018

Major Tasks

This assignment consists of the following tasks:

• To acquire a better understanding of clustering methods.

• To learn to implement K-means for clustering.

• To learn to implement Fuzzy Clustering EM for clustering.

• To learn to use a DBSCAN model for clustering.

Clustering Methods

You are required to implement the three clustering methods and report the cost time. Please employ euclidean distance as the distance metric.

• K-means You are required to implement the clustering method K-means by yourself. You are not allowed to use any existing K-means package (But basic computation package is okay, eg., Numpy, Scipy). You should test your algorithm with different K values, K ∈ {2, 10, 20, 30}.

• Fuzzy Clustering EM You are required to implement the Fuzzy clustering method by using the EM algorithm (reference to slides 11ClusAdvanced.pdf). You are not allowed to use any existing EM package (But basic computation package is okay, eg., Numpy, Scipy). Please test your algorithm by setting K = 2.

• DBSCAN You are required to use the DBSCAN model for clustering. You may use the DBSCAN model implemented by scikit learn. Please test your model on the dataset by setting � = 0.12 and M inP ts = 3.

Data Description

https://github.com/comp4331fall18/sample-code-DM/blob/master/Assignment3-Dataset/dataset.mat. The dataset contains 500 2-dimensions points.