DocumentCode :
3382106
Title :
Semi-supervised fuzzy c-medoids clustering algorithm with multiple prototype representation
Author :
de Melo, Filipe M. ; de Carvalho, Francisco de A. T.
Author_Institution :
Centro de Inf. (CIn), Univ. Fed. de Pernambuco (UFPE), Recife, Brazil
fYear :
2013
fDate :
7-10 July 2013
Firstpage :
1
Lastpage :
7
Abstract :
Semi-supervised clustering is a special form of classification that uses a large amount of unlabeled data together with labeled data to achieve better classification results. This paper introduces a semi-supervised fuzzy clustering algorithm of relational data with multiple prototype representation (SS-CLAMP) that aims to furnish a partition and a set of prototypes for each fuzzy cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an adequacy criterion that measures the fit between the fuzzy clusters and their representatives in a competitive way and that takes into account pairwise constraints must-link and cannot-link. Experiments with real-valued data sets show the usefulness of the proposed algorithm.
Keywords :
fuzzy set theory; learning (artificial intelligence); matrix algebra; pattern classification; pattern clustering; SS-CLAMP; adequacy criterion; dissimilarity matrix; fuzzy clusters; multiple prototype representation; pairwise constraints; real-valued data sets; relational data; semi-supervised fuzzy c-medoids clustering algorithm; Accuracy; Algorithm design and analysis; Clustering algorithms; Iris; Partitioning algorithms; Prototypes; Vectors; Constrained clustering; fuzzy clustering; relational data; semi-supervised clustering;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Fuzzy Systems (FUZZ), 2013 IEEE International Conference on
Conference_Location :
Hyderabad
ISSN :
1098-7584
Print_ISBN :
978-1-4799-0020-6
Type :
conf
DOI :
10.1109/FUZZ-IEEE.2013.6622374
Filename :
6622374
Link To Document :
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