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Synthesizing Knowledge: A Cluster Analysis Approach Using Event Covering

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2 Author(s)

An event-covering method [1] for synthesizing knowledge gathered from empirical observations is presented. Based on the detection of statistically significant events, knowledge is synthesized through the use of a special clustering algorithm. This algorithm, employing a probabilistic information measure and a subsidiary distance, is capable of clustering ordered and unordered discrete-valued data that are subject to noise perturbation. It consists of two phases: cluster initiation and cluster refinement. During cluster initiation, an analysis of the nearest-neighbor distance distribution is performed to select a criterion for merging samples into clusters. During cluster refinement, the samples are regrouped using the event-covering method, which selects subsets of statistically relevant events. For performance evaluation, we tested the algorithm using both simulated data and a set of radiological data collected from normal subjects and spina bifida patients.

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IEEE Transactions on Systems, Man, and Cybernetics  (Volume:16 ,  Issue: 2 )