Abstract
This paper proposes a weighted (W) k-prototype (KP) Multi Objective Genetic Algorithm (MOGA) (W - KP - MOGA) that can automatically evolve feature weights (based on importance of features in cluster) and clustering solutions. Here we are hybridizing KP with MOGA. Minimization of Homogeneity (H) and maximization of Separation (S) are two measures of optimization. For comparison purpose we have also implemented KP and KP - MOGA. Testing by different real world data set with different clustering validity indices shows the superiority of W - KP - MOGA.
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