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An Empirical Comparative Study of Novel Clustering Algorithms for Class Imbalance Learning

  • Ch. N. Santhosh Kumar
  • K. Nageswara Rao
  • A. Govardhan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 380)

Abstract

Data mining is the process of discovering knowledge from the vast data sources. In Data mining, classification and clustering are the two broad branches of study. In Clustering, K-means algorithm is one of the bench mark algorithms used for numerous applications. The popularity of k-means algorithm is due to its efficient and low usage of memory. One of the short comings of k-means algorithm is degradation of performance, when applied to imbalance distributed data. The results of cluster size generated by k-means are relatively uniform, in spite of the input data with non-uniform cluster sizes, which is defined as “uniform effect” in the literature. This paper proposes several novel algorithms to solve the above said problem. The proposed algorithms are compared with each other. The experiments conducted with the proposed algorithm on eleven UCI datasets with evaluation metrics show that proposed algorithms are effective to solve the problem of “uniform effect.”

Keywords

Imbalanced data K-means clustering algorithms Oversampling Uniform effect 

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Copyright information

© Springer India 2016

Authors and Affiliations

  • Ch. N. Santhosh Kumar
    • 1
  • K. Nageswara Rao
    • 2
  • A. Govardhan
    • 3
  1. 1.Department of CSEJNTU-HyderabadHyderabadIndia
  2. 2.PSCMR College of Engineering and TechnologyVijayawadaIndia
  3. 3.CSE & SITJNTU HyderabadHyderabadIndia

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