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Hierarchical conceptual clustering based on quantile method for identifying microscopic details in distributional data

Author

Listed:
  • Kadri Umbleja

    (Tokyo Denki University)

  • Manabu Ichino

    (Tokyo Denki University)

  • Hiroyuki Yaguchi

    (Tokyo Denki University)

Abstract

Symbolic data is aggregated from bigger traditional datasets in order to hide entry specific details and to enable analysing large amounts of data, like big data, which would otherwise not be possible. Symbolic data may appear in many different but complex forms like intervals and histograms. Identifying patterns and finding similarities between objects is one of the most fundamental tasks of data mining. In order to accurately cluster these sophisticated data types, usual methods are not enough. Throughout the years different approaches have been proposed but they mainly concentrate on the “macroscopic” similarities between objects. Distributional data, for example symbolic data, has been aggregated from sets of large data and thus even the smallest microscopic differences and similarities become extremely important. In this paper a method is proposed for clustering distributional data based on these microscopic similarities by using quantile values. Having multiple points for comparison enables to identify similarities in small sections of distribution while producing more adequate hierarchical concepts. Proposed algorithm, called microscopic hierarchical conceptual clustering, has a monotone property and has been found to produce more adequate conceptual clusters during experimentation. Furthermore, thanks to the usage of quantiles, this algorithm allows us to compare different types of symbolic data easily without any additional complexity.

Suggested Citation

  • Kadri Umbleja & Manabu Ichino & Hiroyuki Yaguchi, 2021. "Hierarchical conceptual clustering based on quantile method for identifying microscopic details in distributional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 15(2), pages 407-436, June.
    • Handle: RePEc:spr:advdac:v:15:y:2021:i:2:d:10.1007_s11634-020-00411-w
    DOI: 10.1007/s11634-020-00411-w
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    References listed on IDEAS

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    1. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    2. Lawrence Hubert, 1972. "Some extensions of Johnson's hierarchical clustering algorithms," Psychometrika, Springer;The Psychometric Society, vol. 37(3), pages 261-274, September.
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