Classification Techniques in WEKA: A Review
DOI:
https://doi.org/10.26438/ijcse/v5i8.4952Keywords:
Classification, Weka, Data MiningAbstract
Due to the Internet Revolution there has been a data explosion in recent decades. This is due to the easy availability of Internet at any place and time. Therefore it has become very important to extract relevant information from these explosion of data. Data Mining is extraction or mining of useful information from large amount of data. This can be done manually, semi-automatic or automatically. With an enormous of data stored in databases and data warehouse there is need for development of powerful tools to get meaningful data. Data Mining has many tasks such as Classification, Clustering, etc but Classification has gained much importance. Classification is to classify the data into groups based on its characteristics. WEKA is widely used data mining tool. Here a comparison of various algorithms available in WEKA for classification tasks is done. The dataset considered is iris and various parameters considered for evaluation include accuracy, kappa statistics, mean absolute error and root mean square error. 10 mostly used algorithms are compared. Accuracy is given in terms of CCI (Correctly Classified Instances) and ICI (Incorrectly Classified Instances).
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