Fuzzy Co-clustering Algorithm for Multi-source Data Mining

DOI

10.2991/asum.k.210827.016How to use a DOI?

Keywords

Data mining, multi-source, fuzzy co-clustering, multi-view, multi-subspace

Abstract

The development of information and communication technology has motivated multi-source data to become more common and publicly available. Compared to traditional data that describe objects from a single-source, multi-source data is semantically richer, more useful, however many-feature, more uncertain, and complex. Since traditional clustering algorithms cannot handle such data, multi-source clustering has become a research hotspot. Most existing multi-source clustering methods are developed from single-source clustering by extending the objective function or building combination models. In fact, the fuzzy clustering methods handle the uncertainty data better than the hard clustering methods. Recently, fuzzy co-clustering has proven effective in the many-feature data processing due to the possibility of isolating the uncertainty present in each feature. In this paper, a novel multi-source data mining algorithm based on a modified fuzzy co-clustering algorithm and two penalty terms is proposed, which is called Multi-source Fuzzy Co-clustering Algorithm (MSFCoC). Experimental results on various multi-source datasets indicate that the proposed MSFCoC algorithm outper-forms existing state-of-the-art clustering algorithms.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Le Thi Cam Binh
AU  - Pham Van Nha
AU  - Pham The Long
PY  - 2021
DA  - 2021/08/30
TI  - Fuzzy Co-clustering Algorithm for Multi-source Data Mining
BT  - Joint Proceedings of the 19th World Congress of the International Fuzzy Systems Association (IFSA), the 12th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT), and the 11th International Summer School on Aggregation Operators (AGOP)
PB  - Atlantis Press
SP  - 117
EP  - 124
SN  - 2589-6644
UR  - https://doi.org/10.2991/asum.k.210827.016
DO  - 10.2991/asum.k.210827.016
ID  - Binh2021
ER  -