Towards improving a similarity search approach
Author: 
Yong ShiAuthors Info & Claims
Yong ShiAuthors Info & ClaimsArticle No.: 49, Pages 1 - 3
Abstract
In this paper, we present continuous research on data analysis based on our previous work on similarity search problems. PanKNN [13] is a novel technique which explores the meaning of K nearest neighbors from a new perspective, redefines the distances between data points and a given query point Q, and efficiently and effectively selects data points which are closest to Q. It can be applied in various data mining fields. In this paper, we present our approach to improving the PanKNN algorithm using the Shrinking concept. Shrinking[15] is a data preprocessing technique which optimizes the inner structure of data inspired by the Newton's Universal Law of Gravitation[11] in the real world. This improved approach can assist to improve the performance of existing data analysis approaches.
References
[1]
White D. A. and Jain R. Similarity Indexing with the SS-tree. In Proceedings of the 12th Intl. Conf. on Data Engineering, pages 516--523, New Orleans, Louisiana, February 1996.
[2]
E. Achtert, C. Böhm, P. Kröger, P. Kunath, A. Pryakhin, and M. Renz. Efficient reverse k-nearest neighbor search in arbitrary metric spaces. In SIGMOD '06, pages 515--526, New York, NY, USA, 2006. ACM.
[3]
C. C. Aggarwal. Towards meaningful high-dimensional nearest neighbor search by human-computer interaction. In ICDE, 2002.
[4]
C. C. Aggarwal, A. Hinneburg, and D. A. Keim. On the surprising behavior of distance metrics in high dimensional space. Lecture Notes in Computer Science, 1973, 2001.
[5]
D. A. Berchtold S., Keim and H.-P. Kriegel. The X-tree: An index structure for high-dimensional data. In VLDB'96, pages 28--39, Bombay, India, 1996.
[6]
K. Beyer, J. Goldstein, R. Ramakrishnan, and U. Shaft. When is "nearest neighbor" meaningful? In International Conference on Database Theory 99, pages 217--235, Jerusalem, Israel, 1999.
[7]
B. Cui, H. Shen, J. Shen, and K. Tan. Exploring bit-difference for approximate KNN search in high-dimensional databases. In Australasian Database Conference, 2005., 2005.
[8]
R. Fagin, R. Kumar, and D. Sivakumar. Efficient similarity search and classification via rank aggregation, 2003.
[9]
A. Gionis, P. Indyk, and R. Motwani. Similarity search in high dimensions via hashing. In The VLDB Journal, pages 518--529, 1999.
[10]
A. Hinneburg, C. C. Aggarwal, and D. A. Keim. What is the nearest neighbor in high dimensional spaces? In The VLDB Journal, pages 506--515, 2000.
[11]
Rothman, Milton A. The laws of physics. New York, Basic Books, 1963.
[12]
T. Seidl and H.-P. Kriegel. Optimal multi-step k-nearest neighbor search. SIGMOD Rec., 27(2):154--165, 1998.
[13]
Y. Shi and L. Zhang. A dimension-wise approach to similarity search problems. In the 4th International Conference on Data Mining (DMIN'08), 2008.
[14]
R. Weber, H.-J. Schek, and S. Blott. A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In Proc. 24th Int. Conf. Very Large Data Bases, VLDB, pages 194--205, 24--27 1998.
[15]
Yong Shi, Yuqing Song and Aidong Zhang. A shrinking-based clustering approach for multidimensional data. In IEEE Transactions on Knowledge and Data Engineering (TKDE), pages 1389--1403, 2005.
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Published In
April 2010
488 pages
ISBN:9781450300643
DOI:10.1145/1900008
- Conference Chair:
- H. Conrad Cunningham,
- Program Chairs:
- Paul Ruth,
- Nicholas A. Kraft
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Published: 15 April 2010
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