Clustering as an example of optimizing arbitrarily chosen objective functions
Authors: Budka, M.
Volume: 457
Pages: 177-186
ISBN: 9783642342998
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. © Springer-Verlag Berlin Heidelberg 2013.
https://eprints.bournemouth.ac.uk/20472/
Source: Scopus
Clustering as an Example of Optimizing Arbitrarily Chosen Objective Functions
Authors: Budka, M.
Volume: 457
Pages: 177-186
ISBN: 978-3-642-34299-8
DOI: 10.1007/978-3-642-34300-1_17
https://eprints.bournemouth.ac.uk/20472/
Source: Web of Science (Lite)
Clustering as an example of optimizing arbitrarily chosen objective functions
Authors: Budka, M.
Editors: Nguyen, N., Trawinski, B., Katarzyniak, R. and Geun-Sik, J.
Volume: 457
Pages: 177-186
Publisher: Springer Berlin / Heidelberg
ISBN: 978-3-642-34299-8
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.
https://eprints.bournemouth.ac.uk/20472/
Source: Manual
Preferred by: Marcin Budka
Clustering as an Example of Optimizing Arbitrarily Chosen Objective Functions.
Authors: Budka, M.
Editors: Nguyen, N.T., Trawinski, B., Katarzyniak, R.P. and Jo, G.
Volume: 457
Pages: 177-186
Publisher: Springer
ISBN: 978-3-642-34299-8
DOI: 10.1007/978-3-642-34300-1_17
https://eprints.bournemouth.ac.uk/20472/
https://doi.org/10.1007/978-3-642-34300-1
Source: DBLP
Clustering as an example of optimizing arbitrarily chosen objective functions
Authors: Budka, M.
Editors: Nguyen, N., Trawinski, B., Katarzyniak, R. and Geun-Sik, J.
Volume: 457
Pages: 177-186
Publisher: Springer Berlin / Heidelberg
ISBN: 978-3-642-34299-8
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.
https://eprints.bournemouth.ac.uk/20472/
Source: BURO EPrints