Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/5087
Title: | Clustering evaluation in high-dimensional data | Authors: | Tomašev, Nenad Radovanović, Miloš |
Issue Date: | 1-Jan-2016 | Journal: | Unsupervised Learning Algorithms | Abstract: | © Springer International Publishing Switzerland 2016. Clustering evaluation plays an important role in unsupervised learning systems, as it is often necessary to automatically quantify the quality of generated cluster configurations. This is especially useful for comparing the performance of different clustering algorithms as well as determining the optimal number of clusters in clustering algorithms that do not estimate it internally. Many clustering quality indexes have been proposed over the years and different indexes are used in different contexts. There is no unifying protocol for clustering evaluation, so it is often unclear which quality index to use in which case. In this chapter, we review the existing clustering quality measures and evaluate them in the challenging context of high-dimensional data clustering. High-dimensional data is sparse and distances tend to concentrate, possibly affecting the applicability of various clustering quality indexes. We analyze the stability and discriminative power of a set of standard clustering quality measures with increasing data dimensionality. Our evaluation shows that the curse of dimensionality affects different clustering quality indexes in different ways and that some are to be preferred when determining clustering quality in many dimensions. | URI: | https://open.uns.ac.rs/handle/123456789/5087 | ISBN: | 9783319242118 | DOI: | 10.1007/978-3-319-24211-8_4 |
Appears in Collections: | PMF Publikacije/Publications |
Show full item record
SCOPUSTM
Citations
18
checked on Aug 12, 2023
Page view(s)
16
Last Week
9
9
Last month
0
0
checked on May 10, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.