yasirutishan / kmeans_clustering

The focus of this coursework is to assess your understanding of unsupervised machine learning techniques. You are required to write MATLAB code to implement the Kmeans clustering algorithm. This is an extension of Lab 3 on Kmeans clustering.

K-Means Clustering

Task 2.1 – Data Preparation

1. DATA: To get data for K means clustering, you use MATLAB code “gen_clusterdata”. This code generates your personal data matrix. Use MATLAB command: X=gen_clusterdata(ID), where ID is your student number. The code generates a data matrix X of four columns representing features and N rows representing objects (cases).

2. Data analysis.

a) Report N - the total number of rows (objects, cases) in your data. b) For each column (feature) from 1 to 4 report: the mean, the standard deviation and the histogram. c) Report the covariance matrix (4x4) and the correlation matrix (4x4).

Task 2.2 – K Means

1- Given the number of clusters as 3 (i.e., K=3), implement Kmeans clustering and then repeat the same procedure (i.e., use iteration) to evaluate different number of clusters (i.e., K=3, 4, and 5) to find out the optimal number of classes that achieve the best performance.

2- For each K value, report the mean performance using the Silhouette measure and plot the Silhouette for each cluster (each K value) as shown in Figure 1

3- What is the stopping criteria for Kmeans clustering? Plot the clusters and the cluster centroids, which should be similar as in Figure 2 (Note: Figure 2 is an example).

4- Report (via MATLAB code) the best number of clusters and explain why?

5- From your observation and analysis, what are the limitations or drawbacks of Kmeans clustering?