PERCEPTION BASED DOCUMENT CATEGORIZATION USING K-MODE ALGORITHM

Categorization is the development in which objects are recognized, differentiated and understood. Categorization implies that objects are grouped as categories, usually for some precise purpose. There are many categorization theories and techniques such as conventional categorization, conceptual clustering, and Prototype theory. Conceptual Clustering is a data mining (machine learning) technique used to situate data elements into related groups without advance acquaintance of the group definitions. This correspondence describes extensions to the K-modes algorithm for clustering categorical data. The simple identical dissimilarity measure for categorical objects, allows the use of K-modes paradigm to attain a cluster with strong intra similarity and competently cluster large categorical data sets. By exploiting the semantic formation of the sentences in documents, a better text clustering result is achieved