## Clustering as an example of optimizing arbitrarily chosen objective functions

**Authors: **Budka, M.

**Volume:** 457

**Pages:** 177-186

**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