Clustering or cluster analysis is the process of grouping the data into classes or clusters, so that objects within a cluster have high similarity in comparison to one another but are very dissimilar to objects in other clusters . Clustering is a method of unsupervised learning, and a common technique for statistical data analysis. Data clustering algorithms may be hierarchical which find successive clusters using previously established clusters.
The three very important things in Clustering are:
- To select the variables and parameters for Clustering
- The Distance Measurement
- Clustering criteria
Advantages of cluster analysis:
- Cluster analysis is a good way for quick review of data, especially if the objects are classified into many groups. It is easy for users to assign or nominate themselves into a cluster they would most like to compare with in a cluster database because each cluster is clearly named with understandable terms.
- Cluster Analysis provides a simple profile of individuals . Given a number of analysis units, for example school size, student ethnicity, region.
- Cluster Analysis also suggests how groups of units are determined such that units within groups are similar in some respect and unlike those from other groups
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