Cluster Analysis -Figure 1

Cluster Analysis

Cluster analysis (first used by Tryon, 1939) is an exploratory data analysis tool for solving classification problems. Clustering is the classification of similar objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters), so that the data in each subset (ideally) share some common trait - often proximity according to some defined distance measure Its objective is to sort cases (people, things, events, etc) into groups, or clusters, so that the degree of association is strong between members of the same cluster and weak between members of different clusters. Each cluster thus describes, in terms of the data collected, the class to which its members belong; and this description may be abstracted through use from the particular to the general class or type.

Cluster analysis is thus a tool of discovery.It may reveal associations and structure in data which, though not previously evident, nevertheless are sensible and useful once found. The results of cluster analysis may contribute to the definition of a formal classification scheme, such as a taxonomy for related animals, insects or plants; or suggest statistical models with which to describe populations; or indicate rules for assigning new cases to classes for identification and diagnostic purposes; or provide measures of definition, size and change in what previously were only broad concepts; or find exemplars to represent classes.

Machine learning typically regards data clustering as a form of unsupervised learning.Besides the term cluster analysis (or just clustering), there are a number of terms with similar meanings, including:

• Data clustering,
• Automatic classification,
• Numerical taxonomy,
• Botryology
• Typological analysis.

First Blog.....

This is my first blogging in this site.And this wnt be an ad-hoc blogging centre.I will be using this more as an information Junkyard.A one stop location where I can store and retrieve relevant information.