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.
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.
| Original language | English |
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| Title of host publication | Advances in Intelligent Data Analysis XI |
| Publisher | Springer Nature Link |
| Pages | 35-44 |
| ISBN (Electronic) | 978-3-642-34156-4 |
| ISBN (Print) | 978-3-642-34155-7 |
| DOIs | |
| Publication status | Published - 2012 |
| Event | 11th Int Symposium, IDA 2012 - Helsinki, Finland Duration: 25 Oct 2012 → 27 Oct 2012 |
Publication series
| Name | Lecture Notes in Computer Science |
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| Volume | 7619 |
Conference
| Conference | 11th Int Symposium, IDA 2012 |
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| Country/Territory | Finland |
| City | Helsinki |
| Period | 25/10/12 → 27/10/12 |