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Versatile Hyper-Elliptic Clustering Approach for Streaming Data Based on One-Pass-Thrown-Away Learning

Author

Listed:
  • Niwan Wattanakitrungroj

    (Chulalongkorn University)

  • Saranya Maneeroj

    (Chulalongkorn University)

  • Chidchanok Lursinsap

    (Chulalongkorn University)

Abstract

Finding patterns or clusters in streaming data is very important in the present information mining. The most critical issue is the huge amount of data versus the limited size of storage space. In the previous works, the essential information of huge data was represented by subsets of data, grid summarization, or spherical function. Those forms of data representation are not compact enough to capture the topology of the arriving data points and may lead to the lack of information for generating the accurate cluster result. In this work, we proposed a new versatile hyper-elliptic clustering algorithm, called VHEC, to cluster the streaming data in one-pass-thrown-away fashion in order to preserve the original topology of data space. To cope with the problem of one-pass-thrown-away clustering, a new set of elliptic micro-cluster parameters, i.e. boundary, density, direction, intra-distance and inter-distance, was introduced. Furthermore, a feasible technique for merging two micro-clusters was developed. The proposed parameters and one-pass-throw-away clustering algorithm were tested against several benchmark data sets and structural clustering data sets. Our performance was compared with existing algorithms. Regardless of different sizes, shapes, and densities, VHEC outperformed the other previous data stream clustering algorithms on both synthetic and real data sets. Moreover, VHEC is more significantly robust to streaming speed and incoming data sequence than the other compared algorithms in terms of purity, Rand index, and adjusted Rand index measures.

Suggested Citation

  • Niwan Wattanakitrungroj & Saranya Maneeroj & Chidchanok Lursinsap, 2017. "Versatile Hyper-Elliptic Clustering Approach for Streaming Data Based on One-Pass-Thrown-Away Learning," Journal of Classification, Springer;The Classification Society, vol. 34(1), pages 108-147, April.
    • Handle: RePEc:spr:jclass:v:34:y:2017:i:1:d:10.1007_s00357-017-9222-1
    DOI: 10.1007/s00357-017-9222-1
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    References listed on IDEAS

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    1. Vasudha Bhatnagar & Sharanjit Kaur & Laurent Mignet, 2009. "A Parameterized Framework for Clustering Streams," International Journal of Data Warehousing and Mining (IJDWM), IGI Global, vol. 5(1), pages 36-56, January.
    2. Douglas Steinley & Michael J. Brusco, 2007. "Initializing K-means Batch Clustering: A Critical Evaluation of Several Techniques," Journal of Classification, Springer;The Classification Society, vol. 24(1), pages 99-121, June.
    3. Lawrence Hubert & Phipps Arabie, 1985. "Comparing partitions," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 193-218, December.
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