CLUSTERING AND CLUSTER VALIDATION IN DATA MINING

Clustering is one of the fundamental operations in data mining. Clustering is widely used in solving business problems such as customer segmentation and fraud detection. In real applications of clustering, we are required to perform three tasks: partitioning data sets into clusters, validating the clustering results and interpreting the clusters. Various clustering algorithms have been designed for the first task. Few techniques are available for cluster validation in data mining. The third task is application dependent and needs domain knowledge to understand the clusters. In this paper, we present a few techniques for the first two tasks. We first discuss the family of the k-means type algorithms, which are mostly used in data mining. Then we present a visual method for cluster validation. This method is based on the Fastmap data projection algorithm and its enhancement. Finally, we present a method to combine a clustering algorithm and the visual cluster validation method to interactively build classification models.

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