research-article

SubCOID: an attempt to explore cluster-outlier iterative detection approach to multi-dimensional data analysis in subspace

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Yong ShiAuthors Info & Claims

ACMSE '08: Proceedings of the 46th annual ACM Southeast Conference

Pages 132 - 135

https://doi.org/10.1145/1593105.1593139

Published: 28 March 2008 Publication History

Abstract

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. Clusters and outliers should be treated as the concepts of the same importance in data analysis. In our previous work [22] we proposed a cluster-outlier iterative detection algorithm in full data space. However, in high dimensional spaces, for a given cluster or outlier, not all dimensions may be relevant to it. In this paper we extend our work in subspace area, tending to detect the clusters and outliers in another perspective for noisy data. Each cluster is associated with its own subset of dimensions, so is each outlier. The partition, subsets of dimensions and qualities of clusters are detected and adjusted according to the intra-relationship within clusters and the inter-relationship between clusters and outliers, and vice versa. This process is 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.

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Cited By

Shi YZhang L(2011)COID: A cluster–outlier iterative detection approach to multi-dimensional data analysisKnowledge and Information Systems10.1007/s10115-010-0323-y28:3(709-733)Online publication date: 1-Sep-2011
https://dl.acm.org/doi/10.1007/s10115-010-0323-y
Shi YCunningham HRuth PKraft N(2010)Towards improving subspace data analysisProceedings of the 48th annual ACM Southeast Conference10.1145/1900008.1900093(1-4)Online publication date: 15-Apr-2010
https://dl.acm.org/doi/10.1145/1900008.1900093

Index Terms

SubCOID: an attempt to explore cluster-outlier iterative detection approach to multi-dimensional data analysis in subspace
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
  2. Information systems applications
    1. Data mining
      1. Clustering

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cover image ACM Other conferences

ACMSE '08: Proceedings of the 46th annual ACM Southeast Conference

March 2008

548 pages

ISBN:9781605581057

DOI:10.1145/1593105

Copyright © 2008 ACM.

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Published: 28 March 2008

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ACM SE08

ACM SE08: ACM Southeast Regional Conference

March 28 - 29, 2008

Alabama, Auburn

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Cited By

Shi YZhang L(2011)COID: A cluster–outlier iterative detection approach to multi-dimensional data analysisKnowledge and Information Systems10.1007/s10115-010-0323-y28:3(709-733)Online publication date: 1-Sep-2011
https://dl.acm.org/doi/10.1007/s10115-010-0323-y
Shi YCunningham HRuth PKraft N(2010)Towards improving subspace data analysisProceedings of the 48th annual ACM Southeast Conference10.1145/1900008.1900093(1-4)Online publication date: 15-Apr-2010
https://dl.acm.org/doi/10.1145/1900008.1900093

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