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

Zippy Erlich, Roy Gelbard, Israel Spiegler

Research output: Contribution to journalArticlepeer-review


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 ('I' 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.

Original languageEnglish
Pages (from-to)187-197
Number of pages11
JournalInformation Systems Frontiers
Issue number2
StatePublished - 2002


  • Binary representation
  • Clustering
  • Data mining
  • Similarity


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