Chapter 4 Cluster Analysis
Section 1 Clustering Basics
Page 5 Clustering Algorithms
Objectives
The objectives of this section are:
define clustering
outline the various application of clustering
delve into the various types of clustering
define the various types of clusters
introduce some popular clustering algorithms
Outcomes
By the time you have completed this section you will be able to:
list some of the applications of cluster analysis
define clustering
list the various types of clusters and clustering
list some of the well known clustering algorithms
Copyright © Rakesh Verma 2009
Types of Clustering Algorithms
- Partitioning-based clustering: are algorithms that determine all the clusters at once in most cases.
- K-means clustering
- K-medoids clustering
- EM (expectation maximization) clustering
- Hierarchical clustering: these algorithms find successive clusters using previously established ones.
- Divisive clustering is a top down approach.
- Agglomerative clustering is a bottom up approach.
- Density-Based Methods: these clustering algorithms are used to help discover arbitrary-shaped clusters. A cluster is defined as a region in which the density of data objects exceeds some threshold.
- DBSCAN
- OPTICS
The rest of this chapter focuses on the three major techniques that fall into the three categories states above in cluster analysis.
