Clustering spatial data using random walks

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Clustering spatial data using random walks

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International Conference on Knowledge Discovery and Data Mining archive
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining table of contents

San Francisco, California

Pages: 281 - 286

Year of Publication: 2001

ISBN:1-58113-391-X

Authors

David Harel	The Weizmann Institute of Science, Rehovot, Israel
Yehuda Koren	The Weizmann Institute of Science, Rehovot, Israel

Sponsors

SIGMOD: ACM Special Interest Group on Management of Data
AAAI : American Association for Artificial Intelligence

Publisher

ACM New York, NY, USA

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Downloads (6 Weeks): 9, Downloads (12 Months): 91, Citation Count: 12

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ABSTRACT

Discovering significant patterns that exist implicitly in huge spatial databases is an important computational task. A common approach to this problem is to use cluster analysis. We propose a novel approach to clustering, based on the deterministic analysis of random walks on a weighted graph generated from the data. Our approach can decompose the data into arbitrarily shaped clusters of different sizes and densities, overcoming noise and outliers that may blur the natural decomposition of the data. The method requires only O(n log n) time, and one of its variants needs only constant space.

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	Matthew T. Dickerson , David Eppstein, Algorithms for proximity problems in higher dimensions, Computational Geometry: Theory and Applications, v.5 n.5, p.277-291, Jan. 1996 [doi>10.1016/0925-7721(95)00009-7]
	2	V. Estivill-Castro and I. Lee,"AUTOCLUST: Automatic Clustering via Boundary Extraction for Mining Massive Point- Data Sets", 5th International Conference on Geocomputation, GeoComputation CD-ROM: GC049, ISBN 0-9533477-2-9.
	3	Y. Gdalyahu, D. Weinshall and M. Werman, "Stochastic Image Segmentation by Typical Cuts", Proceedings IEEE Conference on Computer Vision and Pattern Recognition, 1999, pp. 588-601.
	4	Y. Koren , D. Harel, Clustering Spatial Data Using Random Walks,, Weizmann Science Press of Israel, Jerusalem, Israel, 2001
	5	Anil K. Jain , Richard C. Dubes, Algorithms for clustering data, Prentice-Hall, Inc., Upper Saddle River, NJ, 1988
	6	George Karypis , Eui-Hong (Sam) Han , Vipin Kumar, Chameleon: Hierarchical Clustering Using Dynamic Modeling, Computer, v.32 n.8, p.68-75, August 1999 [doi>10.1109/2.781637]
	7	X. Xu , M. Ester, H.P. Kriegel and J. Sander, "Clustering and Knowledge Discovery in Spatial Databases", Vistas in Astronomy, 41 (1997), 397-403.

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	Diansheng Guo , Donna J. Peuquet , Mark Gahegan, ICEAGE: Interactive Clustering and Exploration of Large and High-Dimensional Geodata, Geoinformatica, v.7 n.3, p.229-253, September 2003
	Yu Qian , Kang Zhang, A customizable hybrid approach to data clustering, Proceedings of the 2003 ACM symposium on Applied computing, March 09-12, 2003, Melbourne, Florida
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	Yu Qian , Kang Zhang, Discovering spatial patterns accurately with effective noise removal, Proceedings of the 9th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery, June 13, 2004, Paris, France
	Güneş Erkan, Language model-based document clustering using random walks, Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics, p.479-486, June 04-09, 2006, New York, New York
	Yu Qian , Fang Qiu , Jie Chang , Kang Zhang, Visualization-informed noise elimination and its application in processing high-spatial-resolution remote sensing imagery, Computers & Geosciences, v.34 n.1, p.35-52, January, 2008
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	Pasi Franti , Olli Virmajoki , Ville Hautamaki, Fast Agglomerative Clustering Using a k-Nearest Neighbor Graph, IEEE Transactions on Pattern Analysis and Machine Intelligence, v.28 n.11, p.1875-1881, November 2006
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