Evaluation of Different Classification Techniques for WEB Data

Abstract

The growth of data-mining applications such as classification and clustering has shown the need for machine learning algorithms to be applied to large scale data. In this paper we present the comparison of different classification techniques using Waikato Environment for Knowledge Analysis or in short, WEKA. WEKA is open source software which consists of a collection of machine learning algorithms for data mining tasks. The aim of this paper is to examine the performance of different classification methods for a set of large data. The algorithm which have been tested are J48, SMO, Part, OneR, ZeroR and Navies Bayes Algorithm. The Syskill and webert we page rating data [11] with a total data of 1660 and a dimension of 332 rows and 5columns will be used to test and validate the differences between the classification methods or algorithms.