Weka: A Tool for Data preprocessing, Classification, Ensemble, Clustering and Association Rule Mining

TLDR: The fundamentals of data mining steps like preprocessing the data, feature selection (to select the relevant features and removing the irrelevant and redundant features), classification and evaluation of different classifier models using WEKA tool are given.

Abstract: basic principle of data mining is to analyze the data from different perspectives, classify it and recapitulate it. Data mining has become very popular in each and every application. Though we have large amount of data but we don't have useful information in every field. There are many data mining tools and software to facilitate us the useful information. This paper gives the fundamentals of data mining steps like preprocessing the data (removing the noisy data, replacing the missing values etc.), feature selection (to select the relevant features and removing the irrelevant and redundant features), classification and evaluation of different classifier models using WEKA tool. The WEKA tool is not useful for only one type of application, though it can be used in various applications. This tool consists of various algorithms for feature selection, classification and clustering as well. Keywordsfeature selection, classification, clustering, evaluation of classifier models, evaluation of cluster models.