K-means clustering approach is the most commonly used approach to reduce the sum of intra-cluster differences. But there is problem regarding the selection of centroid in k means clustering algorithm. Centroid can be poor or best depending upon the data. Therefore, there is a probablility of selecting good or bad centroid. So, in case of poor centroid selection, data does not get clustered in proper manners. To overcome this problem, we have used Min-max based K-means clustering algorithm along with ANN (Minimum- maximum based artificial neural network). The ANN algorithm overcomes the pitfalls of Min-max based K-means algorithm (Poor selection of centroid). In our research we have used Min-max K-means algorithm along with ANN to find out the exact category according to the labeled input data. Here, ANN is firstly trained with labeled input data. On the basis of training, testing phase is done to determine the accurate output for labeled input data. The enhancement in the accuracy of the proposed work from the existing work is approximately 16.47%.

Clustering, K-mean, Min-Max, ANN

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