Data Distribution Aware Classification Algorithm based on K-Means
Tamer Tulgar, Ali Haydar, İbrahim Erşan · International Journal of Advanced Computer Science and Applications · 2017
Giving data driven decisions based on precise data analysis is widely required by different businesses. For this purpose many different data mining strategies exist. Nevertheless, existing strategies need attention by researchers so that they can be adapted to the modern data analysis needs. One of the popular algorithms is K-Means. This paper proposes a novel improvement to the classical K-Means classification algorithm. It is known that data characteristics like data distribution, high-dimensionality, the size, the sparseness of the data, etc. have a great impact on the success of the K-Means clustering, which directly affects the accuracy of classification. In this study, the K-Means algorithm was modified to remedy the algorithm’s classification accuracy degradation, which is observed when the data distribution is not suitable to be clustered by data centroids, where each centroid is represented by a single mean. Specifically, this paper proposes to intelligently include the effect of variance based on the detected data distribution nature of the data. To see the performance improvement of the proposed method, several experiments were carried out using different real datasets. The presented results, which are achieved after extensive experiments, prove that the proposed algorithm improves the classification accuracy of KMeans. The achieved performance was also compared against several recent classification studies which are based on different classification schemes.