Biased box sampling - a density-biased sampling for clustering

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Biased box sampling - a density-biased sampling for clustering

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Symposium on Applied Computing archive
Proceedings of the 2007 ACM symposium on Applied computing table of contents

Seoul, Korea

SESSION: Data mining table of contents

Pages: 445 - 446

Year of Publication: 2007

ISBN:1-59593-480-4

Authors

Ana Paula Appel	University of São Paulo at São Carlos -- Brazil
Adriano Arantes Paterlini	University of São Paulo at São Carlos -- Brazil
Elaine P. M. de Sousa	University of São Paulo at São Carlos -- Brazil
Agma J. M. Traina	University of São Paulo at São Carlos -- Brazil
Caetano Traina, Jr.	University of São Paulo at São Carlos -- Brazil

Sponsor

SIGAPP: ACM Special Interest Group on Applied Computing

Publisher

ACM New York, NY, USA

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ABSTRACT

This paper presents the BBS - Biased Box Sampling algorithm, a technique that combines dimensionality reduction with biased sampling, which aims at keeping the skewed clustering from the original data.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	M. Ester, H.-P. Kriegel, J. Sander, and X. Xu. A density-based algorithm for discovering clusters in large spatial databases with noise. In Proceedings of the Second International Conference on KDD-96, pages 226--231. AAAI Press, 1996.
	2	E. P. M. d. Sousa, C. Traina Jr., A. J. M. Traina, and C. Faloutsos. How to use fractal dimension to find correlations between attributes. In First Workshop on Fractals and Self-similarity in Data Mining: Issues and Approaches (in conjunction with 8th ACM SIGKDD), pages 26--30, Edmonton, Alberta, Canada, 2002. ACM Press.