Day 2 - Team H

Cluster Analysis:

Clustering procedures

There are several types of clustering methods:

1. Non-Hierarchical clustering (also called k-means clustering)

First determine a cluster center, and then group all objects that are within a certain distance.

2. Hierarchical clustering

Objects are organized into a hierarchical structure as part of the procedure.

Examples:

Divisive clustering - start by treating all objects as if they are part of a single large cluster, then divide the cluster into smaller and smaller clusters

Agglomerative clustering - start by treating each object as a separate cluster, and then group them into bigger and bigger clusters

Examples:

Centroid methods - clusters are generated that maximize the distance between the centers of clusters (a centroid is the mean value for all the objects in the cluster)

Variance methods - clusters are generated that minimize the within-cluster variance

Example:

Ward’s Procedure - clusters are generated that minimize the squared Euclidean distance to the center mean

Linkage methods - cluster objects based on the distance between them

Examples:

Single Linkage method - cluster objects based on the minimum distance between them (also called the nearest neighbor rule)

Complete Linkage method - cluster objects based on the maximum distance between them (also called the furthest neighbor rule)

Average Linkage method - cluster objects based on the average distance between all pairs of objects (one member of the pair must be from a different cluster)

Author:

Balram Singla