{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,5,9]],"date-time":"2025-05-09T15:05:41Z","timestamp":1746803141813,"version":"3.40.5"},"reference-count":18,"publisher":"Wiley","license":[{"start":{"date-parts":[[2018,6,3]],"date-time":"2018-06-03T00:00:00Z","timestamp":1527984000000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Advances in Fuzzy Systems"],"published-print":{"date-parts":[[2018,6,3]]},"abstract":"<jats:p>A novel hybrid clustering method, named <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M2\"><mml:mi>K<\/mml:mi><mml:mi>C<\/mml:mi><\/mml:math>-Means clustering, is proposed for improving upon the clustering time of the Fuzzy <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M3\"><mml:mrow><mml:mi>C<\/mml:mi><\/mml:mrow><\/mml:math>-Means algorithm. The proposed method combines <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M4\"><mml:mrow><mml:mi>K<\/mml:mi><\/mml:mrow><\/mml:math>-Means and Fuzzy <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M5\"><mml:mrow><mml:mi>C<\/mml:mi><\/mml:mrow><\/mml:math>-Means algorithms into two stages. In the first stage, the <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M6\"><mml:mrow><mml:mi>K<\/mml:mi><\/mml:mrow><\/mml:math>-Means algorithm is applied to the dataset to find the centers of a fixed number of groups. In the second stage, the Fuzzy <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M7\"><mml:mrow><mml:mi>C<\/mml:mi><\/mml:mrow><\/mml:math>-Means algorithm is applied on the centers obtained in the first stage. Comparisons are then made between the proposed and other algorithms in terms of time processing and accuracy. In addition, the mentioned clustering algorithms are applied to a few benchmark datasets in order to verify their performances. Finally, a class of Minkowski distances is used to determine the influence of distance on the clustering performance.<\/jats:p>","DOI":"10.1155\/2018\/2634861","type":"journal-article","created":{"date-parts":[[2018,6,3]],"date-time":"2018-06-03T19:31:49Z","timestamp":1528054309000},"page":"1-8","source":"Crossref","is-referenced-by-count":10,"title":["<mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\" id=\"M1\"><mml:mi>K<\/mml:mi><mml:mi>C<\/mml:mi><\/mml:math>-Means: A Fast Fuzzy Clustering"],"prefix":"10.1155","volume":"2018","author":[{"given":"Israa","family":"Abdzaid Atiyah","sequence":"first","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5079-7025","authenticated-orcid":true,"given":"Adel","family":"Mohammadpour","sequence":"additional","affiliation":[{"name":"Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"S. 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