Paper Info
Title
Cluster Prototypes and Fuzzy Memberships Jointly Leveraged Cross-Domain Maximum Entropy Clustering
Abstract
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.
Year
DOI
Venue
2016
10.1109/TCYB.2015.2399351
Cybernetics, IEEE Transactions  
Keywords
Field
DocType
cross-domain clustering,maximum entropy clustering (mec),privacy protection,transfer learning,validity index,optimization,linear programming,prototypes,clustering algorithms,convergence,indexes,entropy
Data mining,Transfer of learning,Robustness (computer science),Rand index,FLAME clustering,Linear programming,Artificial intelligence,Cluster analysis,Mathematical optimization,Fuzzy logic,Principle of maximum entropy,Machine learning,Mathematics
Journal
Volume
Issue
ISSN
PP
99
2168-2267
Citations 
PageRank 
References 
9
0.42
43
Authors
7
Name
Order
Citations
PageRank
pengjiang qian1305.48
Yizhang Jiang238227.24
Zhaohong Deng364735.34
lingzhi hu4110.80
shouwei sun5120.80
Shitong Wang61485109.13
Raymond F. Muzic Jr.7284.48