{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T06:21:07Z","timestamp":1778221267590,"version":"3.51.4"},"reference-count":30,"publisher":"World Scientific Pub Co Pte Lt","issue":"05","funder":[{"name":"the Ministry of Science and Technology of the Republic of China, Taiwan","award":["107-2221-E-182-081-MY3"],"award-info":[{"award-number":["107-2221-E-182-081-MY3"]}]},{"name":"the Ministry of Science and Technology of the Republic of China, Taiwan","award":["108-2221-E-005-059"],"award-info":[{"award-number":["108-2221-E-005-059"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Soft. Eng. Knowl. Eng."],"published-print":{"date-parts":[[2020,5]]},"abstract":"<jats:p> Most clustering algorithms build disjoint clusters. However, clusters might be overlapped because documents may belong to two or more categories in the real world. For example, a paper discussing the Apple Watch may be categorized into either 3C, Fashion, or even Clothing and Shoes. Therefore, overlapping clustering algorithms have been studied such that a resource can be assigned to one or more clusters. Formal Concept Analysis (FCA), which has many practical applications in information science, has been used in disjoin clustering, but has not been studied in overlapping clustering. To make overlapping clustering possible by using FCA, we propose an approach, including two types of transformation. From the experimental results, it shows that the proposed fuzzy overlapping clustering performed more efficiently than existing overlapping clustering methods. The positive results confirm the feasibility of the proposed scheme used in overlapping clustering. Also, it can be used in applications such as recommendation systems. <\/jats:p>","DOI":"10.1142\/s0218194020500229","type":"journal-article","created":{"date-parts":[[2020,6,26]],"date-time":"2020-06-26T11:28:04Z","timestamp":1593170884000},"page":"631-647","source":"Crossref","is-referenced-by-count":5,"title":["Categorization of Multiple Documents Using Fuzzy Overlapping Clustering Based on Formal Concept Analysis"],"prefix":"10.1142","volume":"30","author":[{"given":"Yi-Hui","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Information Management, Chang Gung University, Taoyuan 33302, Taiwan"},{"name":"Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, Kaohsiung, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Eric Jui-Lin","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ya-Wen","family":"Cheng","sequence":"additional","affiliation":[{"name":"Department of Management Information Systems, National Chung Hsing University, Taichung, Taiwan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"219","published-online":{"date-parts":[[2020,6,26]]},"reference":[{"key":"S0218194020500229BIB001","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/978-1-4614-3223-4_4","volume-title":"Mining Text Data","author":"Aggarwal C. 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