Synthesizing Knowledge: A Cluster Analysis Approach Using Event Covering
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 mea...
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Published in | IEEE transactions on systems, man, and cybernetics Vol. 16; no. 2; pp. 251 - 259 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
New York, NY
IEEE
01.03.1986
Institute of Electrical and Electronics Engineers |
Subjects | |
Online Access | Get full text |
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Summary: | 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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ISSN: | 0018-9472 2168-2909 |
DOI: | 10.1109/TSMC.1986.4308945 |