Clustering algorithm

Clustering is a tool for data analysis, which solves classification problems. Its object is to distribute cases (people, objects , events etc.) into groups, so that the degree of association to be strong between members of the same cluster and weak between members of different clusters. This way each cluster describes, in terms of data collected, the class to which its members belong. Clustering is discovery tool. 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. Whatever business you're in, the chances are that sooner or later you will run into a classification problem. Cluster analysis might provide the methodology to help you solve it.

In short: The algorithm Clustering attempts to find natural groups of components, based on some similarity.

The example below demonstrates the clustering of padlocks of same kind. There are a total of 10 padlocks which are of three different colors. We are interested in clustering of padlocks of the three different kind into three different groups.

The padlocks of same kind are clustered into a group as shown below:

Thus, we see clustering means grouping of data or dividing a large data set into smaller data sets of some similarity. Clustering algorithm is included in BI2M application. See examples of using Clustering in BI2M.