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Journal of Zhejiang University SCIENCE A 2004 Vol.5 No.11 P.1405~1412

http://doi.org/10.1631/jzus.2004.1405


Using Greedy algorithm: DBSCAN revisited II


Author(s):  YUE Shi-hong, LI Ping, GUO Ji-dong, ZHOU Shui-geng

Affiliation(s):  Institute of Industrial Process Control, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   Shyue@iipc.zju.edu.cn

Key Words:  DBSCAN algorithm, Greedy algorithm, Density-skewed cluster


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YUE Shi-hong, LI Ping, GUO Ji-dong, ZHOU Shui-geng. Using Greedy algorithm: DBSCAN revisited II[J]. Journal of Zhejiang University Science A, 2004, 5(11): 1405~1412.

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Abstract: 
The density-based clustering algorithm presented is different from the classical Density-Based Spatial Clustering of Applications with Noise (DBSCAN) (Ester et al., 1996), and has the following advantages: first, greedy algorithm substitutes for R*-tree (Bechmann et al., 1990) in DBSCAN to index the clustering space so that the clustering time cost is decreased to great extent and I/O memory load is reduced as well; second, the merging condition to approach to arbitrary-shaped clusters is designed carefully so that a single threshold can distinguish correctly all clusters in a large spatial dataset though some density-skewed clusters live in it. Finally, authors investigate a robotic navigation and test two artificial datasets by the proposed algorithm to verify its effectiveness and efficiency.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

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[2] Dash, M., Liu, H., Xu, X., 2001. ‘1+1>2’: Merging Distance and Density Based Clustering. 7th Int. Conf. DASFAA’ 01, HK, p.118-202.

[3] Ester, M., Kriegel, H.P., Sander, H., Xu, X., 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Datasets with Noise. Proc. 2nd Int. Conf. KDDD. Portland, Oregon, p.1232-1239.

[4] Halkidi, M., Batistakis, Y., Vazirgiannis, M., 2002. Clustering validity checking methods: Part II. SIGMOD Record, 31(4):51-62.

[5] Han, J., 2001. Data Mining. Morgan Kaufmann Publishers, USA, p.242-266.

[6] Krishnapuram, R., Keller, J.M., 1993. A possibilistic approach to clustering. IEEE Trans. Fuzzy Syst., 1(2):98-106.

[7] Krishnapuram, R., Keller, J.M., 1998. An unified views: fuzzy robust clustering. IEEE Trans. Fuzzy Syst., 5(2):270-293.

[8] Lozano, S., Dobado, D., Larraneta, J., 2002. Modified fuzzy c-means algorithm for cellular manufacturing. Fuzzy Sets Syst., 126:23-32.

[9] Nakamura, E., Kehtarnavaz, N., 1998. Determining number of clusters and prototype locations via multi-scale clustering. Pattern Recognition Letters, 19(3):1265-1283.

[10] Skieyca, S., 1990. Minimum Vertex Cover, Implementing Discrete Mathematics: Combinatory and Graph Theory Wit Mathematic. Addison-Wesley, MA, p.234-245.

[11] Thshihiro, F., 2000. Approximation algorithms for submodular set cover with applications. IEICE Trans. Inf. & Syst., 18(3):156-166.

[12] Yue, S.H., Li, P., Zhou, S.G., Gu, Y.K., 2004. Using statistics: DBSCAN revisited I. http://www.cise.zju.edu.cn/iipc/shyue.

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