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Data Mining by Means of Binary Representation: A Model for Similarity and Clustering

Author

Listed:
  • Zippy Erlich

    (The Open University)

  • Roy Gelbard

    (Tel Aviv University)

  • Israel Spiegler

    (Tel Aviv University)

Abstract

In this paper we outline a new method for clustering that is based on a binary representation of data records. The binary database relates each entity to all possible attribute values (domain) that entity may assume. The resulting binary matrix allows for similarity and clustering calculation by using the positive (‘1’ bits) of the entity vector. We formulate two indexes: Pair Similarity Index (PSI) to measure similarity between two entities and Group Similarity Index (GSI) to measure similarity within a group of entities. A threshold factor for each attribute domain is defined that is dependent on the domain but independent of the number of entities in the group. The similarity measure provides simplicity of storage and efficiency of calculation. A comparison of our similarity index to other indexes is made. Experiments with sample data indicate a 48% improvement of group similarity over standard methods pointing to the potential and merit of the binary approach to clustering and data mining.

Suggested Citation

  • Zippy Erlich & Roy Gelbard & Israel Spiegler, 2002. "Data Mining by Means of Binary Representation: A Model for Similarity and Clustering," Information Systems Frontiers, Springer, vol. 4(2), pages 187-197, July.
    • Handle: RePEc:spr:infosf:v:4:y:2002:i:2:d:10.1023_a:1016002919937
    DOI: 10.1023/A:1016002919937
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    Citations

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    Cited by:

    1. Roy Gelbard, 2013. "“Padding” bitmaps to support similarity and mining," Information Systems Frontiers, Springer, vol. 15(1), pages 99-110, March.
    2. Ran Etgar & Yuval Cohen, 2022. "Roadmap Optimization: Multi-Annual Project Portfolio Selection Method," Mathematics, MDPI, vol. 10(9), pages 1-23, May.

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