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U. Maulik, S. Bandyopadhyay, Genetic algorithm-based clustering technique, Pattern Recognition 33 (9) (2000) 455.

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Genetic Algorithms And Cross-Correlation Clustering Of Time Series - Baragona (2000)   (Correct)

....National Research Council (C.N.R. Italy. 2 1. Introduction Genetic algorithms have been proposed in cluster analysis in conjunction with the well known K means algorithm to provide good starting points and avoid that local optima may be reached instead of the global one (Scheunders, 1997; Maulik and Bandyopadhyay, 2000). Clustering methods other than the K means procedure have been benefited as well from interaction with genetic algorithms (Baragona, Calzini and Battaglia, 1999; Tseng and Yang, 2000) Clustering of time series, however, may be tackled from several points of view. It may be of interest either ....

....for p m a wider range is considered. Most figures are suggested which are based on the thorough study of the simple genetic algorithm, where binary chromosomes and single point crossover are used. To stay on the ground of the clustering applications of the genetic algorithm, Scheunders (1997) Maulik and Bandyopadhyay (2000), and 27 Tseng and Yang (2000) all recommended p c =0.8, whilst the figures for p m were 0.05, 0.001, and 0.1 respectively. It has to be noted, however, that in the first two papers the genetic algorithm were considered in conjunction with the K means algorithm, and the last one presented a ....

Maulik U., Bandyopadhyay S. (2000) Genetic algorithm-based clustering technique, Pattern Recognition, 33, 1455-1465.


Multiresolution Genetic Clustering Algorithm for Texture.. - Chang-Tsun Li Randy (2003)   (Correct)

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U. Maulik, S. Bandyopadhyay, Genetic algorithm-based clustering technique, Pattern Recognition 33 (9) (2000) 455.


Evolutionary Multiobjective Clustering - Handl, Knowles (2004)   (Correct)

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U. Maulik and S. Bandyopadhyay. Genetic algorithm-based clustering technique. Pattern Recognition, 33:1455--1465, 2000.

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