Semi-supervised clustering algorithm for data exploration

This source preferred by Hamid Bouchachia

This data was imported from DBLP:

Authors: Bouchachia, A. and Pedrycz, W.

Editors: Bilgiç, T., Baets, B.D. and Kaynak, O.

http://www.informatik.uni-trier.de/~ley/db/conf/ifsa/ifsa2003.html

Journal: IFSA

Volume: 2715

Pages: 328-337

Publisher: Springer

DOI: 10.1007/3-540-44967-1_39

This data was imported from Scopus:

Authors: Bouchachia, A. and Pedrycz, W.

Journal: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)

Volume: 2715

Pages: 328-337

ISSN: 0302-9743

© 2003 Springer-Verlag Berlin Heidelberg. This paper is concerned with clustering of data that is partly labelled. It discusses a semi-supervised clustering algorithm based on a modified fuzzy CMeans (FCM) objective function. Semi-supervised clustering finds its application in different situations where data is neither entirely nor accurately labelled. The novelty of this approach is the fact that it takes into consideration the structure of the data and the available knowledge (labels) of patterns. The objective function consists of two components. The first concerns the unsupervised clustering while the second keeps the relationship between classes (available labels) and the clusters generated by the first component. The balance between the two components is tuned by a scaling factor. The algorithm is experimentally evaluated.

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