A Hybrid Data Mining Approach To Evaluate Performance of Classification And Clustering Methods Implemented On Weka Platform

Abstract

Data mining is the process of finding of hidden information from a huge amount of data. Data mining analyzing the data from different source and convert it into meaningful information. Data mining is a new powerful technology that helps business to focus on important information like future trends, decision making, customer choice etc. A target dataset is prepared before applying the data mining algorithm. The common source of data is the data warehouse. Pre processing is needed to analyze the data sets before applying the data mining. Data mining is also defined as the process of discovering patterns in data. The process must be automatic or (more usually) semi-automatic. The patterns discovered must be meaningful in that they lead to some advantage, usually an economic advantage. The data invariably present in substantial quantities. Different types of learning techniques can be used, including classification, association rules, clustering, attribute selection, normalization, instance based measures and decision trees. Selection of a learning technique is a difficult task that depends on the database and the types of desired results. Raw data is useless without techniques to extract information from it. Main reasons to use data mining are too much data & too little information and need to extract useful information from the data and to interpret the data.