Clustering techniques have a wide use and importance nowadays and this importance tends to increase as the amount of data grows. K-means is a simple technique for clustering analysis. Its aim is to find the best division of n entities into k groups (called clusters), so that total distance between the group's members and corresponding centroid, irrespective of the group is minimized. Each entity belongs to the cluster with the nearest mean. It results into a partitioning of the data space into Voronoi Cells. This paper is about the implementation of k-means clustering using crop yield records by Weka Interface. The data has been taken from the website "Agricultural Statistics of India". This papers also includes detailed result analysis of rice data after demonstration via Weka Interface.

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K-means Clustering Euclidean Distance Spatial Data Mining Weka Interface