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A framework for automatic clustering of semantic models

  • J. Akoka
  • I. Comyn-Wattiau
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 823)

Abstract

This article is concerned with the development, application and implication of automatic clustering of Entity-Relationship (E-R) diagrams and object models, called here semantic models. The development of such technique is discussed with particular reference to those areas where clustering of semantic models may assist progress in aiding the understanding of an organization's data. Topics such as the role of a new clustering algorithm, how best to represent distances between the elements of the model to be clustered, and the organizational context in which this clustering technique is deployed are addressed. Applications of automatic clustering in several areas and their potential as a communication, documentation and design tools for ER diagrams and object oriented models are described and discussed.

Keywords

Message Passing Semantic Model Semantic Distance Visual Distance Automatic Cluster 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 1994

Authors and Affiliations

  • J. Akoka
    • 1
  • I. Comyn-Wattiau
    • 1
    • 2
  1. 1.ESSECCergy CedexFrance
  2. 2.Laboratoire PRiSMUniversité de VersaillesVersaillesFrance

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