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A New K-means-Based Algorithm for Automatic Clustering and Outlier Discovery

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Information and Communication Technology for Intelligent Systems

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 107))

Abstract

K-means is one of the most popular partition-based clustering algorithms that partition data objects based on attributes/features into K number of groups or clusters. In this paper, we address the major issues affecting the performance of k-means clustering algorithm. We have proposed as well as implemented a new k-means-based clustering algorithm which forms clusters by detecting and removing both global and local outliers and automatically converging into optimal clusters which are formed by a two-part process of splitting the initial clusters into subclusters based on criterion at local level and, in the second part, merging the clusters that satisfy the nearness criterion. Experiments show that our algorithm is able to automatically generate optimal number of clusters of different sizes and shapes which are free from global and local outliers.

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Acknowledgements

We are thankful to Ms. Sana Balotrawala and Mr. Rushabh Shah, students of M.C.A., Department of Computer Science, Gujarat University, for helping with implementation.

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Authors and Affiliations

  1. School of Computer Studies, Ahmedabad University, Ahmedabad, Gujarat, India

    Trushali Jambudi

  2. Department of Computer Science, Gujarat University, Ahmedabad, Gujarat, India

    Savita Gandhi

Authors
  1. Trushali Jambudi
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  2. Savita Gandhi
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Corresponding author

Correspondence to Trushali Jambudi .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, India

    Suresh Chandra Satapathy

  2. Sabar Institute of Technology, Gujarat Technological University, Ahmedabad, Gujarat, India

    Amit Joshi

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Jambudi, T., Gandhi, S. (2019). A New K-means-Based Algorithm for Automatic Clustering and Outlier Discovery. In: Satapathy, S., Joshi, A. (eds) Information and Communication Technology for Intelligent Systems . Smart Innovation, Systems and Technologies, vol 107. Springer, Singapore. https://doi.org/10.1007/978-981-13-1747-7_44

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