The term cluster analysis (first used by Tryon, 1939) encompasses a number of different algorithms and methods for grouping objects of similar kind into respective categories. A general question facing researchers in many areas of inquiry is how to organize observed data into meaningful structures, that is, to develop taxonomies. In other words cluster analysis is an exploratory data analysis tool which aims at sorting different objects into groups in a way that the degree of association between two objects is maximal if they belong to the same group and minimal otherwise. Given the above, cluster analysis can be used to discover structures in data without providing an explanation/interpretation. In other words, cluster analysis simply discovers structures in data without explaining why they exist.
Clustering factors
A simple tutorial on clustering & clustering techniques by Statsoft

The tutorial covers the following:
# Statistical Significance Testing
# Area of Application
# Joining (Tree Clustering)
* Hierarchical Tree
* Distance Measures
* Amalgamation or Linkage Rules
# Two-way Joining
* Introductory Overview
* Two-way Joining
# k-Means Clustering
* Example
* Computations
* Interpretation of results
# EM (Expectation Maximization) Clustering
* Introductory Overview
* The EM Algorithm
Access the tutorial here

By StatSoft, Inc., 1984-2004

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