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Published Articles >> Table of Contents >> Abstract
21st International Conference on Data Engineering (ICDE'05)
pp. 518-519
Towards Exploring Interactive Relationship between Clusters and Outliers in Multi-Dimensional Data Analysis
Yong Shi, State University of New York at Buffalo
Aidong Zhang, State University of New York at Buffalo
Full Article Text:
 
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDE.2005.146
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| Abstract |
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Nowadays many data mining algorithms focus on clustering
methods. There are also a lot of approaches designed
for outlier detection. We observe that, in many situations,
clusters and outliers are concepts whose meanings
are inseparable to each other, especially for those data sets
with noise. Thus, it is necessary to treat clusters and outliers
as concepts of the same importance in data analysis.
In this paper, we present a cluster-outlier iterative detection
algorithm, tending to detect the clusters and outliers
in another perspective for noisy data sets. In this algorithm,
clusters are detected and adjusted according to the
intra-relationship within clusters and the inter-relationship
between clusters and outliers, and vice versa. The adjustment
and modification of the clusters and outliers are performed
iteratively until a certain termination condition is
reached. This data processing algorithm can be applied in
many fields such as pattern recognition, data clustering and
signal processing. Experimental results demonstrate the advantages
of our approach.
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 Additional Information
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Citation:
Yong Shi, Aidong Zhang,
"Towards Exploring Interactive Relationship between Clusters and Outliers in Multi-Dimensional Data Analysis,"
icde,
pp. 518-519,
21st International Conference on Data Engineering (ICDE'05),
2005
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