title: |
Cluster Tendency Assessment for Fuzzy Clustering of Incomplete Data |
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publication: |
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part of series: |
Advances in Intelligent Systems Research | |
| pages: | 290 - 297 | |
DOI: |
To be assigned soon (how to use a DOI) | |
author(s): |
Ludmila Himmelspach, Daniel Hommers, Stefan Conrad |
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publication date: |
July 2011 |
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keywords: |
fuzzy cluster analysis, incomplete data,
cluster tendency, cluster validity |
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abstract: |
The quality of results for partitioning clustering algorithms depends on the assumption made on the
number of clusters presented in the data set. Applying clustering methods on real data missing values
turn out to be an additional challenging problem for
clustering algorithms. Fuzzy clustering approaches
adapted to incomplete data perform well for a given
number of clusters. In this study, we analyse different cluster validity functions in terms of applicability on incomplete data on the one hand. On the
other hand we analyse in experiments on several
data sets to what extent the clustering results produced by fuzzy clustering methods for incomplete
data reflect the distribution structure of data. |
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copyright: |
©
Atlantis Press. This article is distributed under the
terms of the Creative Commons Attribution License, which permits
non-commercial use, distribution and reproduction in any medium,
provided the original work is properly cited. |
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full text: |