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In this paper we demonstrate that some recent clustering techniques do not produce sensible clusters and fail to discover knowledge from underlying data sets. Sometimes, they obtain a huge number of clusters from few records and sometimes they obtain only two clusters from many records, where one cluster contains one record and the other cluster contains all remaining records. Interestingly, these...
In this paper we propose a Genetic Algorithm-based clustering technique called GMC that produces high-quality chromosomes in the initial population. The proposed technique also introduces two phases of crossover operation with extensive chromosomes generation aiming to produce high-quality offspring chromosomes and prevent degeneracy. The proposed technique also introduces three steps of mutation...
Many existing GA based clustering techniques generate the number of genes for a chromosome through random selection. Due the random selection there is a high chance of getting poor quality of initial genes in the initial population. A poor-quality initial population is likely to produce a poor-quality clustering solution. We argue that having a set of high quality chromosomes in the initial population...
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