Clustering with proximity knowledge and relational knowledge

  • Authors:

  • Daniel Graves;Joost Noppen;Witold Pedrycz

  • Affiliations:

  • Department of Electrical and Computer Engineering, 9107-116th Street, University of Alberta, Edmonton, Alberta, Canada T6G 2V4;Computing Department, InfoLab21, South Drive, Lancaster University, Lancaster LA1 4WA, UK;Department of Electrical and Computer Engineering, 9107-116th Street, University of Alberta, Edmonton, Alberta, Canada T6G 2V4

  • Venue:
  • Pattern Recognition
  • Year:
  • 2012

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Abstract

In this article, a proximity fuzzy framework for clustering relational data is presented, where the relationships between the entities of the data are given in terms of proximity values. We offer a comprehensive and in-depth comparison of our clustering framework with proximity relational knowledge to clustering with distance relational knowledge, such as the well known relational Fuzzy C-Means (FCM). We conclude that proximity can provide a richer description of the relationships among the data and this offers a significant advantage when realizing clustering. We further motivate clustering relational proximity data and provide both synthetic and real-world experiments to demonstrate both the usefulness and advantage offered by clustering proximity data. Finally, a case study of relational clustering is introduced where we apply proximity fuzzy clustering to the problem of clustering a set of trees derived from software requirements engineering. The relationships between trees are based on the degree of closeness in both the location of the nodes in the trees and the semantics associated with the type of connections between the nodes.