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Proceedings of the Second International Conference on Computer and Communication Technologies pp 181–191Cite as

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

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Part of the book series: Advances in Intelligent Systems and Computing ((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.”

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Author information

Authors and Affiliations

  1. Department of CSE, JNTU-Hyderabad, Hyderabad, A.P, India

    Ch. N. Santhosh Kumar

  2. PSCMR College of Engineering and Technology, Vijayawada, A.P, India

    K. Nageswara Rao

  3. CSE & SIT, JNTU Hyderabad, Hyderabad, A.P, India

    A. Govardhan

Authors
  1. Ch. N. Santhosh Kumar
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  2. K. Nageswara Rao
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  3. A. Govardhan
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Corresponding author

Correspondence to Ch. N. Santhosh Kumar .

Editor information

Editors and Affiliations

  1. Dept. of Computer Science and Engineering, Anil Neerukonda Institute of Technology and Sciences, Vishakapatnam, India

    Suresh Chandra Satapathy

  2. Department of CSE, CMR Technical Campus, Hyderabad, India

    K. Srujan Raju

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

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Santhosh Kumar, C.N., Rao, K.N., Govardhan, A. (2016). An Empirical Comparative Study of Novel Clustering Algorithms for Class Imbalance Learning. In: Satapathy, S., Raju, K., Mandal, J., Bhateja, V. (eds) Proceedings of the Second International Conference on Computer and Communication Technologies. Advances in Intelligent Systems and Computing, vol 380. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2523-2_17

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  • DOI: https://doi.org/10.1007/978-81-322-2523-2_17

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2522-5

  • Online ISBN: 978-81-322-2523-2

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