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- Title
Categorization of Multiple Documents Using Fuzzy Overlapping Clustering Based on Formal Concept Analysis.
- Authors
Chen, Yi-Hui; Lu, Eric Jui-Lin; Cheng, Ya-Wen
- Abstract
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.
- Subjects
RECOMMENDER systems; INFORMATION science; CONCEPTS; FUZZY logic
- Publication
International Journal of Software Engineering & Knowledge Engineering, 2020, Vol 30, Issue 5, p631
- ISSN
0218-1940
- Publication type
Article
- DOI
10.1142/S0218194020500229