Classification Of Data Mining Techniques - 1512 Words

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 t he 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 isShow MoreRelatedData Analysis : Data Mining Essay1087 Words   |  5 PagesData, Data everywhere. It is a precious thing that will last longer than the systems. In this challenging world, there is a high demand to work efficiently without risk of losing any tiny information which might be very important in future. Hence there is need to create large volumes of data which needs to be stored and explored for future analysis. 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