A comparative study of famous classification techniques and data mining tools

Abstract

Data mining is the procedure or technique of drawing out the facts and patterns hidden in huge sum of data and converts it into a readable and understandable form. Data mining has four main modules like classification, association rule analysis, and clustering and sequence analysis. The classification is the major module and is used in many different areas for classification problems. Classification process gives a summary of data investigation which may be utilized to develop models or structures, telling different classes or predict future data trends for improved understanding of the data at maximum. In this survey, various data mining classification techniques and some important data mining tools along with their advantages and disadvantages are presented. Data classification techniques are classified into three categories namely, Eager learners, Lazy learners, and other Classification techniques. Decision tree, Bayesian classification, Rule based classification, Support Vector Machines (SVM), Association rule mining and backpropagation (Neural Networks) are eager learners. The K-Nearest Neighbor (KNN) classification and Case Based Reasoning (CRT) are lazy learners. Other classification techniques include genetic algorithms, fuzzy logic and Rough Set Approach. Here six important data mining tools, basic Eager learner, Lazy learner and other classification techniques for data classification are discussed. The aim of this article is to provide a survey of six famous data mining tools and famous different data mining classification techniques.