Abstract
Complex and heterogeneous data sets can often be interpreted as having multiple clustering, each of which are valid but distinct from the others. Several algorithms involving multiple objective functions have been reported for such alternative clusterings. We propose a genetic algorithm based approach for obtaining valid but diverse clustering. A variable length genetic algorithm approach is used to enable varying number of clusters in each interpretation. A suitable method for population initialization and appropriate crossover and mutation operators are also used. Experimental results on benchmark data sets show that the method is comparable with related alternative clustering techniques.
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Saha, M., Mitra, P. (2014). VLGAAC: Variable Length Genetic Algorithm Based Alternative Clustering. In: Loo, C.K., Yap, K.S., Wong, K.W., Teoh, A., Huang, K. (eds) Neural Information Processing. ICONIP 2014. Lecture Notes in Computer Science, vol 8835. Springer, Cham. https://doi.org/10.1007/978-3-319-12640-1_24
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DOI: https://doi.org/10.1007/978-3-319-12640-1_24
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-12639-5
Online ISBN: 978-3-319-12640-1
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