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Many popular clustering techniques including K-means require various user inputs such as the number of clusters k, which can often be very difficult for a user to guess in advance. Moreover, existing techniques like K-means also have a tendency of getting stuck at local optima. As a result, various evolutionary algorithm based clustering techniques have been proposed. Typically, they choose the initial...
K-means algorithm is very well-known in large data sets of clustering. This algorithm is popular and more widely used for its easy implementation and fast working. However, it is well known that in the k-means algorithm, the user should specify the number of clusters in advance. In order to improve the performance of the K-means algorithm, various methods have been proposed. In this paper, has been...
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