{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,4,13]],"date-time":"2024-04-13T05:10:12Z","timestamp":1712985012457},"reference-count":26,"publisher":"Walter de Gruyter GmbH","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2011,1,1]]},"abstract":"<jats:title>Matrix Neuro-Fuzzy Self-Organizing Clustering Network<\/jats:title><jats:p>In this article the problem of clustering massive data sets, which are represented in the matrix form, is considered. The article represents the 2-D self-organizing Kohonen map and its self-learning algorithms based on the winner-take-all (WTA) and winner-take-more (WTM) rules with Gaussian and Epanechnikov functions as the fuzzy membership functions, and without the winner. The fuzzy inference for processing data with overlapping classes in a neural network is introduced. It allows one to estimate membership levels for every sample to every class. This network is the generalization of a vector neuro- and neuro-fuzzy Kohonen network and allows for data processing as they are fed in the on-line mode.<\/jats:p>","DOI":"10.2478\/v10143-011-0042-1","type":"journal-article","created":{"date-parts":[[2012,2,23]],"date-time":"2012-02-23T02:03:14Z","timestamp":1329962594000},"page":"54-58","source":"Crossref","is-referenced-by-count":3,"title":["Matrix Neuro-Fuzzy Self-Organizing Clustering Network"],"prefix":"10.2478","volume":"45","author":[{"given":"Yevgeniy","family":"Bodyanskiy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Valentyna","family":"Volkova","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mark","family":"Skuratov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"374","reference":[{"key":"1","unstructured":"V. Kuntsevich and M. 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