[1]CHEN Aiguo,WANG Shitong.A maximum entropy-based knowledge transfer fuzzy clustering algorithm[J].CAAI Transactions on Intelligent Systems,2017,12(1):95-103.[doi:10.11992/tis.201602003]
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A maximum entropy-based knowledge transfer fuzzy clustering algorithm

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 12 Number of periods: 2017 1 Page number: 95-103 Column: 学术论文—机器学习 Public date: 2017-02-25
Title:
A maximum entropy-based knowledge transfer fuzzy clustering algorithm
Author(s):
CHEN Aiguo1; 2;  WANG Shitong1
1. School of Digital Media, Jiangnan University, Wuxi 214122, China;
2. Department of Computing, Hong Kong Polytechnic University, Kowloon 999077, China
Keywords:
knowledge transfer; maximum entropy; clustering algorithms; maximum entropy clustering; fuzzy clustering
CLC:
TP274
DOI:
10.11992/tis.201602003
Abstract:
To address the issue of clustering performance degradation when traditional clustering algorithms are applied to insufficient and/or noisy data, a maximum entropy-based knowledge transfer fuzzy clustering algorithm is proposed. This improves the classical maximum entropy clustering algorithm for target domains by leveraging two kinds of knowledge from the source domain, i.e., historical clustering centers and historical degree of membership, into the objective function proposed for clustering insufficient and/or noisy target data. The effectiveness of the proposed algorithm is demonstrated by experiments on several synthetic and two real datasets.
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