DocumentCode :
1957649
Title :
Genetic-based spatial clustering
Author :
Nola, Antonio Di ; Loia, Vincenzo ; Staiano, Antonino
Author_Institution :
Dipt. di Matematica ed Inf., Salerno Univ., Italy
Volume :
2
fYear :
2000
fDate :
2000
Firstpage :
953
Abstract :
We propose a genetic-level clustering methodology able to cluster objects represented by Rp spaces. The unsupervised cluster algorithm is based on a fuzzy clustering c-means method that searches the best fuzzy partition of the universe assuming that the evaluation of each object respect to some features is unknown, but knowing that it belongs to circular region of R2 space
Keywords :
fuzzy set theory; genetic algorithms; pattern recognition; best fuzzy partition; fuzzy clustering c-means method; genetic-based spatial clustering; genetic-level clustering methodology; unsupervised cluster algorithm; Clustering algorithms; Constitution; Data analysis; Fuzzy sets; Genetic algorithms; Object detection; Partitioning algorithms;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on
Conference_Location :
San Antonio, TX
ISSN :
1098-7584
Print_ISBN :
0-7803-5877-5
Type :
conf
DOI :
10.1109/FUZZY.2000.839162
Filename :
839162
Link To Document :
https://search.ricest.ac.ir/dl/search/defaultta.aspx?DTC=49&DC=1957649
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