2006 | OriginalPaper | Buchkapitel
Semi-supervised Fuzzy c-Means Clustering of Biological Data
verfasst von : M. Ceccarelli, A. Maratea
Erschienen in: Fuzzy Logic and Applications
Verlag: Springer Berlin Heidelberg
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Semi-supervised methods use a small amount of labeled data as a guide to unsupervised techniques. Recent literature shows better performance of these methods with respect to totally unsupervised ones even with a small amount of side-information This fact suggests that the use of semi-supervised methods may be useful especially in very difficult and noisy tasks where little
a priori
information is available. This is the case of biological datasets’ classification. The two more frequently used paradigms to include side-information into clustering are
Constrained Clustering
and
Metric Learning
. In this paper we use a
Metric Learning
approach as a preliminary step to fuzzy clustering and we show that
Semi-Supervised Fuzzy Clustering
(SSFC) can be an effective tool for classification of biological datasets. We used three real biological datasets and a generalized version of the Partition Entropy index to validate our results. In all cases tested the metric learning step produced a better highlight of the datasets’ clustering structure.