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The raw data from an Automated Blood Cell Counter is transformed in to a Preprocessed and Flattened data using the preprocessing phases of the Knowledge Discovery in Databases and the transformed data is used to create clusters of the database in this paper. The K-Means algorithm is applied on the database to form various clusters. Twelve thousand records are taken from a clinical laboratory for processing. Associations among the various attributes of the database are generated. General Terms Algorithms.

Clustering the Preprocessed Automated Blood Cell Counter Data using Modified K-means Algorithms and Generation of Association Rules