{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T15:36:23Z","timestamp":1698766583544},"reference-count":20,"publisher":"World Scientific Pub Co Pte Lt","issue":"04","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Unc. Fuzz. Knowl. Based Syst."],"published-print":{"date-parts":[[2004,8]]},"abstract":"<jats:p> It is well known that an intuitionistic fuzzy relation is a generalization of a fuzzy relation. In fact there are situations where intuitionistic fuzzy relations are more appropriate. This paper discusses the fuzzy clustering based on intuitionistic fuzzy relations. On the basis of max -t &amp; min -s compositions, we discuss an n-step procedure which is an extension of Yang and Shih's [17] n-step procedure. A similarity-relation matrix is obtained by beginning with a proximity-relation matrix using the proposed n-step procedure. Then we propose a clustering algorithm for the similarity-relation matrix. Numerical comparisons of three critical max -t &amp; min -s compositions: max -t<jats:sub>1<\/jats:sub> &amp; min -s<jats:sub>1<\/jats:sub>, max -t<jats:sub>2<\/jats:sub> &amp; min -s<jats:sub>2<\/jats:sub> and max -t<jats:sub>3<\/jats:sub> &amp; min -s<jats:sub>3<\/jats:sub>, are made. The results show that max -t<jats:sub>1<\/jats:sub> &amp; min -s<jats:sub>1<\/jats:sub> compositions has better performance. Sometimes, data may be missed with an incomplete proximity-relation matrix. Imputation is a general and flexible method for handling missing-data problem. In this paper we also discuss a simple form of imputation is to estimate missing values by max -t &amp; min -s compositions. <\/jats:p>","DOI":"10.1142\/s0218488504002953","type":"journal-article","created":{"date-parts":[[2004,10,21]],"date-time":"2004-10-21T02:02:01Z","timestamp":1098324121000},"page":"513-529","source":"Crossref","is-referenced-by-count":18,"title":["FUZZY CLUSTERING BASED ON INTUITIONISTIC FUZZY RELATIONS"],"prefix":"10.1142","volume":"12","author":[{"given":"WEN-LIANG","family":"HUNG","sequence":"first","affiliation":[{"name":"Department of Mathematics Education, National Hsinchu Teachers College, Hsin-Chu, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JINN-SHING","family":"LEE","sequence":"additional","affiliation":[{"name":"Chemical Systems Research Division,  Chung-Shan Institute of Science and Technology,  Lungtan, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"CHENG-DER","family":"FUH","sequence":"additional","affiliation":[{"name":"Institute of Statistical Science, Academia Sinica, Taipei, Taiwan, R.O.C."}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(86)80034-3"},{"key":"rf2","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(89)90215-7"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(94)90229-1"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(94)90331-X"},{"key":"rf5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-7908-1870-3"},{"key":"rf6","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4757-0450-1"},{"key":"rf7","first-page":"5","volume":"2","author":"Burillo P.","journal-title":"Mathware and Soft Computing"},{"key":"rf8","first-page":"117","volume":"2","author":"Bustince H.","journal-title":"Mathware and Soft Computing"},{"key":"rf9","doi-asserted-by":"publisher","DOI":"10.1016\/0165-0114(96)84610-0"},{"key":"rf10","doi-asserted-by":"publisher","DOI":"10.1142\/S0218488501000648"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1016\/S0165-0114(98)00235-8"},{"key":"rf12","volume-title":"Pattern Classification and Scene analysis","author":"Duda R. 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