Dept. of Information and Computer Engineering, Chung Yuan Christian University, 200 Chung Pei Road, Chung Li District, Taoyuan City, Taiwan.

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Abstract—Data reduction in data mining is very important when dealing with large dataset. Through data reduction one can increase storage efficiency and reduce the run time of data mining process. One of the methods to reduce the volume of data is to selectively retain a subset of the dataset as the representation of the original dataset. This methods is known as prototype selection. Prototype selection aims to discard the superfluous instances in training set, because superfluous instances will influence the result in data mining. In this study we proposed a hybrid prototype selection method using clustering algorithm and selected the most relevant subset of cluster members. The results showed that the hybrid approach performed better than the original methods used individually.

Index Terms—Data mining, data reduction, fuzzy c-means clustering, prototype selection methods.

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Cite: Shih-Wen Ke, Dewi Wulandari, "Merging CLU and PSR Methods for Accurate Prototype Selection," Journal of Software vol. 11, no. 7, pp. 638-645, 2016.