Classification Of Data Mining Techniques

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Abstract Data mining is the process of extracting hidden information from the large data set. Data mining techniques makes easier to predict hidden patterns from the data. The most popular data mining techniques are classification, clustering, regression, association rules, time series analysis and summarization. Classification is a data mining task, examines the features of a newly presented object and assigning it to one of a predefined set of classes. In this research work data mining classification techniques are applied to disaster data set which helps to categorize the disaster data based on the type of disaster occurred in worldwide for past 10 decade. The experimental comparison has been conducted among Bayes classification algorithms (BayesNet and NaiveBayes) and Rules Classification algorithms (DecisionTable and JRip). The efficiency of these algorithms is measured by using the performance factors; classification accuracy, error rate and execution time. This work is carried out in the WEKA data mining tool. From the experimental result, it is observed that Rules classification algorithm, JRip has produced good classification accuracy compared to Bayes classification algorithms. By comparing the execution time the NaiveBayes classification algorithm required minimum time. Keywords: Disasters, Classification, BayesNet, NaiveBayes, DecisionTable, JRip. I Introduction Data mining is the process of extracting hidden information from the large dataset. Data mining is
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