Cluster Prototypes and Fuzzy Memberships Jointly Leveraged Cross-Domain Maximum Entropy Clustering

The classical maximum entropy clustering (MEC) algorithm usually cannot achieve satisfactory results in the situations where the data is insufficient, incomplete, or distorted. To address this problem, inspired by transfer learning, the specific cluster prototypes and fuzzy memberships jointly lever...

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Bibliographic Details
Published in:IEEE transactions on cybernetics 2016-01, Vol.46 (1), p.181-193
Main Authors: Pengjiang Qian, Yizhang Jiang, Zhaohong Deng, Lingzhi Hu, Shouwei Sun, Shitong Wang, Muzic, Raymond F.
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clustering algorithms
Clusters
Convergence
Cross-domain clustering
Entropy
Fuzzy
Fuzzy logic
Fuzzy set theory
Indexes
Linear programming
Maximum entropy
maximum entropy clustering (MEC)
Optimization
privacy protection
Prototypes
transfer learning
validity index
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Summary:The classical maximum entropy clustering (MEC) algorithm usually cannot achieve satisfactory results in the situations where the data is insufficient, incomplete, or distorted. To address this problem, inspired by transfer learning, the specific cluster prototypes and fuzzy memberships jointly leveraged (CPM-JL) framework for cross-domain MEC (CDMEC) is firstly devised in this paper, and then the corresponding algorithm referred to as CPM-JL-CDMEC and the dedicated validity index named fuzzy memberships-based cross-domain difference measurement (FM-CDDM) are concurrently proposed. In general, the contributions of this paper are fourfold: 1) benefiting from the delicate CPM-JL framework, CPM-JL-CDMEC features high-clustering effectiveness and robustness even in some complex data situations; 2) the reliability of FM-CDDM has been demonstrated to be close to well-established external criteria, e.g., normalized mutual information and rand index, and it does not require additional label information. Hence, using FM-CDDM as a dedicated validity index significantly enhances the applicability of CPM-JL-CDMEC under realistic scenarios; 3) the performance of CPM-JL-CDMEC is generally better than, at least equal to, that of MEC because CPM-JL-CDMEC can degenerate into the standard MEC algorithm after adopting the proper parameters, and which avoids the issue of negative transfer; and 4) in order to maximize privacy protection, CPM-JL-CDMEC employs the known cluster prototypes and their associated fuzzy memberships rather than the raw data in the source domain as prior knowledge. The experimental studies thoroughly evaluated and demonstrated these advantages on both synthetic and real-life transfer datasets.
ISSN:2168-2267
2168-2275
DOI:10.1109/TCYB.2015.2399351