In this paper we propose K-Boost, a novel clustering algorithm basedon a combination of the Furthest-Point-First (FPF) heuristic for solving themetric k-center problem, a {\em stability-based} method for determining thenumber of clusters, and a k-means-like cluster refinement. Experiments showthat \textit{K-Boost} exhibits a good quality/running time tradeoff that makesit ideal for large data sets, with quality measured by several internal andexternal criteria.

K-Boost: a Scalable Algorithm for High-Quality Clustering of Microarray Gene Expression Data / Geraci, F; Leoncini, Mauro; Montangero, Manuela; Pellegrini, M; Renda, M. E.. - In: JOURNAL OF COMPUTATIONAL BIOLOGY. - ISSN 1066-5277. - STAMPA. - 16:(2009), pp. 859-873.

K-Boost: a Scalable Algorithm for High-Quality Clustering of Microarray Gene Expression Data

LEONCINI, Mauro;MONTANGERO, Manuela;
2009

Abstract

In this paper we propose K-Boost, a novel clustering algorithm basedon a combination of the Furthest-Point-First (FPF) heuristic for solving themetric k-center problem, a {\em stability-based} method for determining thenumber of clusters, and a k-means-like cluster refinement. Experiments showthat \textit{K-Boost} exhibits a good quality/running time tradeoff that makesit ideal for large data sets, with quality measured by several internal andexternal criteria.
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K-Boost: a Scalable Algorithm for High-Quality Clustering of Microarray Gene Expression Data / Geraci, F; Leoncini, Mauro; Montangero, Manuela; Pellegrini, M; Renda, M. E.. - In: JOURNAL OF COMPUTATIONAL BIOLOGY. - ISSN 1066-5277. - STAMPA. - 16:(2009), pp. 859-873.
Geraci, F; Leoncini, Mauro; Montangero, Manuela; Pellegrini, M; Renda, M. E.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/612899
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