A unified fuzzy data model: Representation and processing

Avichai Meged, Roy Gelbard

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


A novel fuzzy data representation model which enables data mining with standard tools is introduced. Many data elements in the world are fuzzy in nature. There is an obvious need to represent and process such data effectively and efficiently, using the same standard tools for crisp data that are popular with researchers and practitioners alike. Currently, however, standard tools cannot process or analyze data that are not adequately represented. The comprehensive data representation model put forward here extends principles of binary databases and provides a unified approach to all types of data: discrete and continuous, crisp and fuzzy. The model is illustrated on a baseline dataset and tested in clustering experiments matched against controlled groupings and a real dataset. The tests confirm that the implementation of the model not only enables the use of standard tools but also yields better results as regards segmentation and clustering of fuzzy datasets.

Original languageEnglish
Pages (from-to)78-102
Number of pages25
JournalJournal of Database Management
Issue number1
StatePublished - Jan 2012


  • Binary Databases
  • Clustering
  • Data Mining
  • Data Representation Models
  • Fuzzy Data
  • Fuzzy Databases


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