Dear guest, welcome to this publication database. As an anonymous user, you will probably not have edit rights. Also, the collapse status of the topic tree will not be persistent. If you like to have these and other options enabled, you might ask Pascal Held for a login account.
| This site is powered by Aigaion - A PHP/Web based management system for shared and annotated bibliographies. For more information visit www.aigaion.nl. |
|
A new distance function for Prototype-based Clustering Algorithms in High-Dimensional Spaces
| Type of publication: | Inproceedings |
| Citation: | winkler2011anewdistance |
| Booktitle: | Proceedings of CLADAG 2011 |
| Year: | 2011 |
| Month: | September |
| Location: | Pavia, Italy |
| Abstract: | High dimensional data analysis poses some interesting and counter-intuitive problems. One of these problems is that some clustering algorithms do not work or work only very poorly if the dimensionality is high enough. The reason for this is an effect called distance concentration. In this paper we show that the effect can be countered for prototype based clustering algorithms by using a clever alteration of the distance function. We show the success of this process by applying (but not restricting) it on FCM. A useful side effect is that our method can also be used to estimate the number of clusters in a data set. |
| Keywords: | |
| Authors | |
| Added by: | [GR] |
| Total mark: | 0 |
|
Attachments
|
|
|
|
|
Notes
|
|
|
|
|
|
Topics
|
|
|
|
