Clustering as an example of optimizing arbitrarily chosen objective functions.

Budka, M., 2013. Clustering as an example of optimizing arbitrarily chosen objective functions. In: Nguyen, N., Trawinski, B., Katarzyniak, R. and Geun-Sik, J., eds. Advanced Methods for Computational Collective Intelligence. Springer Berlin / Heidelberg, 177 - 186 .

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DOI: 10.1007/978-3-642-34300-1_17

Abstract

This paper is a reflection upon a common practice of solving various types of learning problems by optimizing arbitrarily chosen criteria in the hope that they are well correlated with the criterion actually used for assessment of the results. This issue has been investigated using clustering as an example, hence a unified view of clustering as an optimization problem is first proposed, stemming from the belief that typical design choices in clustering, like the number of clusters or similarity measure can be, and often are suboptimal, also from the point of view of clustering quality measures later used for algorithm comparison and ranking. In order to illustrate our point we propose a generalized clustering framework and provide a proof-of-concept using standard benchmark datasets and two popular clustering methods for comparison.

Item Type:Book Section
ISBN:978-3-642-34299-8
Series Name:Studies in Computational Intelligence
Volume:457
Number:2013
ISSN:1860-949X
Series Name:Studies in Computational Intelligence
Uncontrolled Keywords: clustering – cluster analysis – optimization – genetic algorithms – particle swarm optimization – general-purpose optimization techniques
Subjects:UNSPECIFIED
Group:School of Design, Engineering & Computing
ID Code:20472
Deposited By:Unnamed user with email symplectic@symplectic
Deposited On:22 Oct 2012 11:59
Last Modified:05 Dec 2012 14:18

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