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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 TermsAlgorithms.

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 meaningful associations between the attributes of the database in this paper. Various Association Rules are generated using general algorithm and apriori algorithm. The apriori algorithm is found to be efficient than the general algorithm. Twelve thousand records are taken from a clinical laboratory for processing.

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