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• W2000543075 abstract "Privacy preserving data mining is a promising topic for utilizing various personal information without fear of information leaks. Fuzzy co-clustering is a fundamental technique for summarizing mutual cooccurrence information among objects and items, and has been demonstrated to be useful in such applications as document analysis and collaborative filtering. In this paper, a secure framework for privacy preserving fuzzy co-clustering is proposed for handling both vertically and horizontally distributed cooccurrence matrices. Personal observation stored in each site is summarized into co-cluster structures with an encryption operation. The advantage of utilizing distributed cooccurrence matrices is demonstrated in several numerical experiments." @default.
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• W2000543075 date "2014-12-01" @default.
• W2000543075 modified "2023-09-25" @default.
• W2000543075 title "Privacy preserving fuzzy co-clustering with distributed cooccurrence matrices" @default.
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• W2000543075 doi "https://doi.org/10.1109/scis-isis.2014.7044660" @default.
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