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The raw Complete Blood Count (CBC) or Full Blood Count (FBC) data from an Automated Blood Cell Counter are collected and transformed in to a Preprocessed and Flattened data using the preprocessing phases of the Knowledge Discovery in Databases. The data is classified into child and adult data sets. The transformed data is used to create clusters of the database in this paper. The K-Means algorithm with two initial mean selection such as first element selection and random element selection is applied on the attributes of the Automated Blood Cell Counter Data to form various clusters. Twelve thousand records are taken from a clinical laboratory for processing. General Terms Algorithms.

international journal of computer applications

Clustering the Age Classified Preprocessed Automated Blood Cell Counter Data using K-Means First Distinct Element Selection and Random Selection Algorithms