Genetically oriented clustering using variable length chromosomes

In most cases, due to the plethora of data values, it is unrealistic for domain experts to mine useful knowledge from the database. Motivated by this, a novel approach for optimized clustering is developed in this paper. The proposed approach is genetically oriented to mine vital information incorporated in large databases avoiding entrapment in local optima and sensitivity to initialization. The aim of the proposed method is to find an optimum set of clusters that can properly classify all training data without much computational burden. The contribution is twofold: firstly, it gleans the valuable information hidden behind the database and secondly, it evolves automatically the appropriate number of cluster centers, as well as the partitioning of the data, without a priori assumptions on the cluster centers. In this paper, the effectiveness of a Genetic Algorithm with variable length chromosomes is demonstrated for clustering data sets into an unknown number of clusters. The flexibility of the proposed variable length Genetic Algorithm to detect the optimum number of clusters and the corresponding partition is evaluated through various experimental tests. The results of the proposed algorithm are compared with those obtained by the wellknown fuzzy c-means algorithm, which is applicable only for a predefined fixed number of clusters and the subtractive clustering method, where data-points are considered as candidate cluster centers.