Improvisation of Clustering by Attribute Reduction Using Bayesian Theorem

Data reduction aims to reduce the dimensionality of large scale data with out losing useful information, is an important topic of knowledge discovery, data clustering and classification. This Paper introduces a novel concept of dependency based attribute reduction using Bayes Theorem. Bayesian Theory is of great interest in Data reduction. Attribute reduction is a data mining approach for detecting and characterizing combinations of attributes or independent variables that interact to influence a dependent or class variable. The basis of this attribute reduction is a method that converts two or more variables or attributes to a single attribute and by calculating the probabilities of their values in deciding the value of class attribute. Hence, the dependent attributes are found and are removed from the original dataset. The end goal is to improve the classification accuracy such that prediction of the class variable is improved over that of the original data with initial attribute set and also reduces the computational time.