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• W4386880136 abstract "Partial multi-view clustering (PMVC) is a hot topic in data mining, where each view suffers from the absence of some data. Although many PMVC algorithms have been presented with appropriate performance, to the best of our knowledge, almost all of them need to be fed with the number of clusters as hyperparameters. Moreover, these existing algorithms only create hard partitions for objects with multiple views, which are often in highly overlapping areas. This ignores ambiguity and uncertainty in object assignment, which likely leads to performance degradation. To address these issues, we propose a novel self-filling evidential clustering algorithm (SFPMEC) for the PMVC problem. Based on the shared coefficient matrix learned from a self-filling process, SFPMEC provides a human-readable chart through which the number of clusters can be easily detected. SFPMEC then derives a credal partition under the framework of evidence theory, improving the fault tolerance of clustering and gaining deeper insight into the data structure. Extensive experiments are conducted to demonstrate the benefits of adopting the self-filling process and evidence theory. Besides, our algorithm shows better performance than other state-of-the-art methods on benchmark datasets1." @default.
• W4386880136 created "2023-09-21" @default.
• W4386880136 creator A5019077444 @default.
• W4386880136 creator A5092016766 @default.
• W4386880136 date "2024-03-01" @default.
• W4386880136 modified "2023-10-09" @default.
• W4386880136 title "Self-filling evidential clustering for partial multi-view data" @default.
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• W4386880136 doi "https://doi.org/10.1016/j.eswa.2023.121614" @default.
• W4386880136 hasPublicationYear "2024" @default.
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