Abstract
Clustering analysis is an important step towards getting insight into new data. Ensemble procedures have been designed in order to obtain improved partitions of a data set. Previous work in domain, mostly empirical, shows that accuracy and a limited diversity are mandatory features for successful ensemble construction. This paper presents a method which integrates unsupervised feature selection with ensemble clustering in order to deliver more accurate partitions. The efficiency of the method is studied on real data sets.
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Breaban, M.E. (2010). Optimized Ensembles for Clustering Noisy Data. In: Blum, C., Battiti, R. (eds) Learning and Intelligent Optimization. LION 2010. Lecture Notes in Computer Science, vol 6073. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13800-3_23
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DOI: https://doi.org/10.1007/978-3-642-13800-3_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13799-0
Online ISBN: 978-3-642-13800-3
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