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Improved Niching and Encoding Strategies for Clustering Noisy Data Sets
Olfa Nasraoui and Elizabeth Leon
Department of Electrical and Computer Engineering, The University of Memphis, Memphis, TN 38152onasraou@memphis.edu
eleon@memphis.edu
Abstract. Clustering is crucial to many applications in pattern recognition, data mining, and machine learning. Evolutionary techniques have been used with success in clustering, but most suffer from several shortcomings. We formulate requirements for efficient encoding, resistance to noise, and ability to discover the number of clusters automatically.
LNCS 3103, p. 1324 f.
lncs@springer.de
© Springer-Verlag Berlin Heidelberg 2004