Skip to main content
Article thumbnail
Location of Repository

Autonomous clustering using rough set theory

By Charlotte Bean and Chandra Kambhampati

Abstract

This paper proposes a clustering technique that minimises the need for subjective\ud human intervention and is based on elements of rough set theory. The proposed algorithm is\ud unified in its approach to clustering and makes use of both local and global data properties to\ud obtain clustering solutions. It handles single-type and mixed attribute data sets with ease and\ud results from three data sets of single and mixed attribute types are used to illustrate the\ud technique and establish its efficiency

Topics: QA
Publisher: Springer Verlag
Year: 2008
OAI identifier: oai:wrap.warwick.ac.uk:61

Suggested articles

Citations

  1. (1974). A fuzzy relative of the ISODATA process and its use in detecting compact, well separated clusters,” doi
  2. (2001). A Knowledge Oriented Clustering Technique Based on Rough Sets”, doi
  3. (1948). A method of establishing groups of equal amplitude in plant sociology based on similarity of species content and its application to the analyses of the vegetation on Danish commons,”
  4. (1908). A Rough Set Based Clustering Method by Knowledge Combination”, doi
  5. (2002). A Rough Set Solution to a Fuzzy Set Problem”, doi
  6. (1988). Algorithms for Clustering Data, doi
  7. (1996). Applied Multivariate Techniques, doi
  8. (1973). Cluster Analysis for Applications, doi
  9. (1965). Cluster Analysis of Multivariate Data: Efficiency Versus Interpretability of
  10. (1993). Cluster Analysis, doi
  11. (1974). Clustering Algorithms, doi
  12. (1999). Data clustering: a review”, doi
  13. (1999). Fuzzy Cluster Analysis. doi
  14. (1963). Hierarchical grouping to optimize an objective function,” doi
  15. (2000). Intelligent control of the hierarchical clustering process”,
  16. (1965). ISODATA, A Novel Method of Data Analysis and Pattern Classification. Menlo Park: Stanford Research Institute.
  17. (1984). K means type algorithms: a generalized convergence theorem and characterization of local optimality”, doi
  18. (2003). Knowledge Oriented Clustering for Decision Support”, doi
  19. (1966). Multidimensional group analysis,” doi
  20. (2000). Multivariate Statistical Methods, A Primer, doi
  21. (1994). New algorithms for solving the fuzzy clustering problem,” doi
  22. (1982). Optimization methods of cluster analysis,” doi
  23. (1981). Pattern Recognition with Fuzzy Objective Function Algorithm, doi
  24. Principles of Numerical Taxonomy, W.W.Freeman, doi
  25. (1989). Rough Fuzzy Sets and Fuzzy Rough Sets”, doi
  26. (1991). Rough Sets, Theoretical Aspects of Reasoning about Data, doi
  27. (1998). Rough Sets: A Tutorial, in: S.Pal and A.Skowron (Eds.), Rough Fuzzy Hybridization: A New Method for Decision Making,
  28. Rough sets”, doi
  29. Some methods of classification and analysis of multivariate observations,”
  30. (1957). The application of computers to taxonomy,” doi
  31. (1992). The discernibility matrices and functions in information systems” in: doi

To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.