Abstract
This paper presents an extension to the genetic fuzzy clustering algorithm proposed by the authors. The original algorithm, which combines the powerful search technique of genetic algorithms with the fuzzy c-means (FCM) algorithm, is extended such that the FCM algorithm was totally embedded in the genetic operators design. Two objective functions are applied as fitness functions: the performance index of a P fuzzy c-partition Jm(P), used on the FCM algorithm, and the partition coe~cient Fc(P), a function commonly used as a measure of cluster validity. The fuzzy c-means and the new proposal for the genetic fuzzy clustering algorithm were compared on generating multiple prototypes. The experimental results show that the use of genetic search improves the quality of the clustering solutions and that the partition coe~cient Fc(P) is a better measure for clustering than the performance index Jm(P).
Original language | English |
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Title of host publication | Advances in Intelligent Data Analysis, Reasoning About Data |
Editors | X. Liu, P. Cohen, M. Berthold |
Publisher | Springer-Verlag |
Pages | 325-335 |
Number of pages | 11 |
Volume | 1280 |
Publication status | Published - 1997 |
Event | Second International Symposium on Intelligent Data Analysis: Advances in Intelligent Data Analysis. Reasoning about Data - London, United Kingdom Duration: 4 Aug 1997 → 4 Aug 1997 http://www.dcs.bbk.ac.uk/archive/ida97/ |
Publication series
Name | Lecture Notes in Computer Science |
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Publisher | Springer-Verlarg |
Volume | 1280 |
Conference
Conference | Second International Symposium on Intelligent Data Analysis |
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Abbreviated title | IDA-97 |
Country/Territory | United Kingdom |
City | London |
Period | 4/08/97 → 4/08/97 |
Internet address |
Keywords
- Image segmentation
- Information analysis
- Algorithms
- Data handling
- Fuzzy clustering
- Fuzzy systems
- Genetic algorithms