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KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
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ECIR'05 Proceedings of the 27th European conference on Advances in Information Retrieval Research
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Data Mining has become one of the most exciting and fastest growing fields in Information Technology. This is due to unprecedented growth-rate of data which is being collected, loaded and stored through World Wide Web and accessed electronically in almost all fields of Human Endeavour. But online retrieval or querying of useful information from these very large databases for Medical diagnosis in hospital information systems (HIS), Biomedical, DNA analysis etc is becoming an increasing scientific challenge. As a result there is an urgent need for sophisticated tools and techniques that can handle extremely large multiple databases. Even it is necessary to globalize Knowledge discovery in databases (KDD) to process and extract useful knowledge's using fully automated techniques. In the field of medicine, as human cannot practically deal with such a huge amount of data, a growing attention has been paid for information processing. To resolve these, it is highly expected to enhance clustering techniques. In this paper we enhance K-means algorithm of Clustering technique by Automatic K generation and optimize the cluster.