Article

HYBRID: from atom-clusters to molecule-clusters

Authors:

Zhou Bing,

Jun-yi Shen,

Qin-ke PengAuthors Info & Claims

FSKD'05: Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I

Pages 1151 - 1160

https://doi.org/10.1007/11539506_144

Published: 27 August 2005 Publication History

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Abstract

This paper presents a clustering algorithm named HYBRID. HYBRID has two phases: in the first phase, a set of spherical atom-clusters with same size is generated, and in the second phase these atom-clusters are merged into a set of molecule-clusters. In the first phase, an incremental clustering method is applied to generate atom-clusters according to memory resources. In the second phase, using an edge expanding process, HYBRID can discover molecule-clusters with arbitrary size and shape. During the edge expanding process, HYBRID considers not only the distance between two atom-clusters, but also the closeness of their densities. Therefore HYBRID can eliminate the impact of outliers while discovering more isomorphic molecule-clusters. HYBRID has the following advantages: low time and space complexity, no requirement of users' involvement to guide the clustering procedure, handling clusters with arbitrary size and shape, and the powerful ability to eliminate outliers.

References

[1]

Raymond T., Hau N. J.: Efficient and effective clustering methods for spatial data mining. Proceedings of the 20th VLDB Conference. Santiago Chile (1994) 144-155.

Digital Library

Google Scholar

[2]

Tian Zhang, Raghu Ramakrishnan, Miron Livny: BIRCH: An Efficient Data Clustering Method for Very Large Databases. Proceedings of the ACM SIGMOD Conference on Management of Data. Montreal Canada (1996) 103-114.

Digital Library

Google Scholar

[3]

Ester M., Kriegel H.P., Sander J.: A density-based algorithm for discovering clusters in large spatial database with noise. Proceedings of 2nd Intl. Conf. on Knowledge Discovering in Databases and Data Mining. Portland USA (1996) 226-231.

Google Scholar

[4]

Guha U., Rastogi R., Shim K.: CURE: an efficient clustering algorithm for large databases. Pergamon Information Systems. 26 (2001) 35-58.

Digital Library

Google Scholar

[5]

Zhou Bing, Shen JunYi, Peng QinKe: Clustering Algorithm Based on Random-Sampling and Cluster-Feature. Journal Of Xi'an Jiaotong University. 37 (2003) 1234-1237.

Google Scholar

[6]

Motwani R., Raghavan P.: Randomized Algorithms. Cambridge University Press, London (1995)

Digital Library

Google Scholar

[7]

George Karypis, Eui-Hong (Sam) Han, Vipin Kumar: CHAMELEON: A Hierarchical Clustering Algorithm Using Dynamic Modeling. COMPUTER. 32 (1999) 68-75.

Digital Library

Google Scholar

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Published In

FSKD'05: Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part I

August 2005

1323 pages

ISBN:3540283129

Editors:
Lipo Wang
School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, Singapore
,
Yaochu Jin
School of Electrical and Electronic Engineering, Honda Research Institute Europe GmbH, Offenbach/Main, Block S1, Nanyang Avenue, Germany

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Springer-Verlag

Berlin, Heidelberg

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Published: 27 August 2005

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