Amorim, R.C. and Fenner, Trevor (2012) Weighting features for partition around medoids using the minkowski metric. In: Jaakko, H. and Frank, K. and Allan, T. (eds.) Advances in Intelligent Data Analysis. Lecture Notes in Computer Science 11 7619. Berlin, Germany: Springer Verlag, pp. 35-44. ISBN 9783642341557.
Abstract
In this paper we introduce the Minkowski weighted partition around medoids algorithm (MW-PAM). This extends the popular partition around medoids algorithm (PAM) by automatically assigning K weights to each feature in a dataset, where K is the number of clusters. Our approach utilizes the within-cluster variance of features to calculate the weights and uses the Minkowski metric. We show through many experiments that MW-PAM, particularly when initialized with the Build algorithm (also using the Minkowski metric), is superior to other medoid-based algorithms in terms of both accuracy and identification of irrelevant features.
Metadata
| Item Type: | Book Section |
|---|---|
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Sarah Hall |
| Date Deposited: | 16 May 2013 09:42 |
| Last Modified: | 09 Aug 2023 12:33 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/6784 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
