Abstract
Minkowski Weighted K-Means is a variant of K-Means set in the Minkowski space, automatically computing weights for features at each cluster. As a variant of K-Means, its accuracy heavily depends on the initial centroids fed to it. In this paper we discuss our experiments comparing six initializations, random and five other initializations in the Minkowski space, in terms of their accuracy, processing time, and the recovery of the Minkowski exponent p.
We have found that the Ward method in the Minkowski space tends to outperform other initializations, with the exception of low-dimensional Gaussian Models with noise features. In these, a modified version of intelligent K-Means excels.
We have found that the Ward method in the Minkowski space tends to outperform other initializations, with the exception of low-dimensional Gaussian Models with noise features. In these, a modified version of intelligent K-Means excels.
| Original language | English |
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| Title of host publication | Advances in Intelligent Data Analysis XI |
| Publisher | Springer Nature |
| Pages | 45-55 |
| 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 |