Recursive Decision Tree Induction Based on Homogeneousness for Data Clustering

Data mining is an analytic process designed to explore data in search of consistent patterns or systematic relationships between variables. To build a model for data mining, both supervised and unsupervised learning techniques are used. In this paper we try to make use of a supervised learning technique called classification tree commonly called decision tree to cluster the similar featured attributes of large datasets. The algorithm takes an image of plotted data values as the input and inducts a decision tree accordingly. The decision factor to form the tree is a measure of homogeneousness of the data pixels in the region. Reverse merging of leaf nodes are done to make clusters based on their domain density.