Synthesizing Knowledge: A Cluster Analysis Approach Using Event Covering

• Published in: IEEE transactions on systems, man, and cybernetics Vol. 16; no. 2; pp. 251 - 259
• Main Authors: Chiu, David K. Y., Wong, Andrew K. C.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.03.1986; Institute of Electrical and Electronics Engineers
• Subjects: Applied sciences; Artificial intelligence; Birth disorders; Clustering algorithms; Computer science; control theory; systems; Event detection; Exact sciences and technology; Hamming distance; Knowledge acquisition; Knowledge based systems; Learning and adaptive systems; Merging; Noise measurement; Performance analysis; Testing; Cluster analysis; Learning; Relevance; Statistical test; Algorithm; Knowledge bare
• ISSN: 0018-9472; 2168-2909
• DOI: 10.1109/TSMC.1986.4308945

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.