@inproceedings{b4b1bad4e7fb4d168ee537e43c8cd61b,
title = "Gene expression data analysis in the membership embedding space: A constructive approach",
abstract = "Exploratory analysis of genomic data sets using unsupervised clustering techniques is often affected by problems due to the small cardinality and high dimensionality of the data set. A way to alleviate those problems lies in performing clustering in an embedding space where each data point is represented by a vector of its memberships to fuzzy sets characterized by a set of probes selected from the data set. This approach has been demonstrated to lead to significant improvements with respect the application of clustering algorithms in the original space and in the distance embedding space. In this paper we propose a constructive technique based on Simulated Annealing able to select sets of probes of small cardinality and supporting high quality clustering solutions.",
author = "M. Filippone and F. Masulli and S. Rovetta",
note = "Publisher Copyright: {\textcopyright} 2006 by World Scientific Publishing Co. Pte. Ltd.; Applied Artificial Intelligence - 7th International Fuzzy Logic and Intelligent Technologies in Nuclear Science Conference, FLINS 2006 ; Conference date: 29-08-2006 Through 31-08-2006",
year = "2006",
doi = "10.1142/9789812774118_0087",
language = "English (US)",
series = "Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006",
publisher = "World Scientific Publishing Co. Pte Ltd",
pages = "617--624",
editor = "Pierre D'Hondt and Kerre, {Etienne E.} and Da Ruan and {De Cock}, Martine and Mike Nachtegael and Fantoni, {Paolo F.}",
booktitle = "Applied Artificial Intelligence - Proceedings of the 7th International FLINS Conference, FLINS 2006",
address = "Singapore",
}