Output details
11 - Computer Science and Informatics
University of Glasgow
Article title
Applying the possibilistic C-means algorithm in kernel-induced spaces
Type
D - Journal article
Title of journal
IEEE Transactions on Fuzzy Systems
Article number
-
Volume number
18
Issue number
3
First page of article
572
ISSN of journal
1063-6706
Year of publication
2010
URL
-
Number of additional authors
2
Additional information
<24>This paper proposes a novel algorithm based on clustering and kernel methods, to cluster data sets without imposing assumptions on the shape and the number of the clusters.
The paper analyses a special case that shares similarities with other popular algorithms for novelty detection, clustering, and density estimation, such as One-Class Support Vector Machines and Kernel Density Estimation. Such analysis suggests a number of advantages of the proposed approach in correctly identifying outliers compared to One-Class Support Vector Machines.
This paper was published in one of the top journal in the field of fuzzy systems and artificial intelligence.
Interdisciplinary
-
Cross-referral requested
-
Research group
None
Citation count
5
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
