Data mining is the process of analyze data from different perspectives and summarizing it into useful information. Clustering is a practical unsupervised data mining task that subdivides an input data set into a desired number of subgroups so that members will have high similarity and the member of different groups have large differences. K-means is a usually used partitioning based clustering technique that tries to find a user specified number of clusters (k), which are represent by their centroids, by minimizing the square error function. Although K-means is easy and can be used for a wide variety of data types, it is rather sensitive to initial positions of cluster centers. There are 2 simple approach to cluster center initialization i.e. either to select the initial values at random, or to select the first k samples of the data points. Both approach cause the algorithm to converge to sub optimal solutions. Genetic algorithm one of the usually used evolutionary algorithms performs global search to find the solution to a clustering problem. The techniques typically starts with a set of randomly generated individuals called the population and creates successive, latest generations of the population by genetic operations such as natural selection, crossover, and mutation. Each one chromosome of the population represent K no. of centroids. Steps of genetic algorithm are repeatedly applied for a no. of generations to search for suitable cluster centers in the feature space such that a similarity metric of the resultant clusters is optimized. K-means and genetic algorithm based data clustering have been compared in this paper on the basis of their functioning principle, advantage and disadvantage with proper example.

Data mining, K-means, Genetic algorithm

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