DocumentCode
3035207
Title
Constrained Intelligent K-Means: Improving Results with Limited Previous Knowledge.
Author
de Amorim, R.C.
Author_Institution
Birkbeck Coll., Univ. of London, London
fYear
2008
fDate
Sept. 29 2008-Oct. 4 2008
Firstpage
176
Lastpage
180
Abstract
It is here presented a new method for clustering that uses very limited amount of labeled data, employees two pairwise rules, namely must link and cannot link and a single wise one, cannot cluster. It is demonstrated that the incorporation of these rules in the intelligent k-means algorithm may increase the accuracy of results, this is proven with experiments where the real number of clusters in the data is unknown to the method.
Keywords
pattern classification; pattern clustering; clustering; constrained intelligent k-means algorithm; labeled data; Clustering algorithms; Computer applications; Data engineering; Data mining; Knowledge engineering; Partitioning algorithms; Semisupervised learning; Clustering; intelligent k-means; k-means; semi supervised clustering;
fLanguage
English
Publisher
ieee
Conference_Titel
Advanced Engineering Computing and Applications in Sciences, 2008. ADVCOMP '08. The Second International Conference on
Conference_Location
Valencia
Print_ISBN
978-0-7695-3369-8
Electronic_ISBN
978-0-7695-3369-8
Type
conf
DOI
10.1109/ADVCOMP.2008.30
Filename
4641014
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