Semi-supervised clustering algorithm for data exploration
Authors: Bouchachia, A. and Pedrycz, W.
Journal: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science)
Volume: 2715
Pages: 328-337
ISBN: 9783540403838
ISSN: 0302-9743
DOI: 10.1007/3-540-44967-1_39
Abstract: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.
Source: Scopus
A semi-supervised clustering algorithm for data exploration
Authors: Bouchachia, A. and Pedrycz, W.
Journal: FUZZY SETS AND SYSTEMS - IFSA 2003, PROCEEDINGS
Volume: 2715
Pages: 328-337
eISSN: 1611-3349
ISSN: 0302-9743
Source: Web of Science (Lite)
A Semi-supervised Clutsering Algorithm for Data Exploration.
Authors: Bouchachia, A. and Pedrycz, W.
Editors: Bilgiç, T., Baets, B.D. and Kaynak, O.
Journal: IFSA
Volume: 2715
Pages: 328-337
Publisher: Springer
https://doi.org/10.1007/3-540-44967-1
Source: DBLP
Preferred by: Hamid Bouchachia