Density Based Spatial Clustering on Manifolds

doi:10.3969/j.issn.1000-1565.2009.06.023

Abstract

Abstract: Density based clustering are effective and basic clustering techniques for data with spatial attributes. Although there are many proposed algorithms and applications for density a based spatial clustering, one of its widely used assumptions is that the data is distributed in a smooth space. Manifolds are approximately curved spaces which are locally like smooth spaces but not smooth spaces. The purpose of this paper is to propose new density based clustering algorithms on manifolds, that is, on curved spaces. The newly proposed algorithms will apply for nonlinear, non-uniform data distribution. A simple performance analysis is presented. Experimental data are given for testing the performances of the new algorithms.

Key words: cluster analysis, manifold learning, data mining, density-based

CLC Number:

TP274

TANG Hao,LIU Xi-yu. Density Based Spatial Clustering on Manifolds[J]. Journal of Hebei University (Natural Science Edition), 2009, 29(6): 658-662.

References

[1] 倪巍伟, 陆介平, 孙志挥, Ni Weiwei, Lu Jieping, Sun Zhihui. 基于向量内积不等式的分布式k均值聚类算法 [J]. 计算机研究与发展 2005.
[2] 何振峰, 熊范纶, He Zhen-feng, XIONG Fan-lun. 结合限制的分隔模型及K-Means算法 [J]. 软件学报 2005.
[3] 杨剑, 李伏欣, 王珏, YANG Jian, LI Fu-Xin, WANG Jue. 一种改进的局部切空间排列算法 [J]. 软件学报 2005.
[4] HAN J, KAMBER M. Data mining, concepts and techniques [M]. 北京:高等教育出版社 2002.
[5] FAYYAD U, PIATETSKY-SHAPIRO G, SMYTH P. Towards a unifying framework [A]. Portland, Oregon, USA 1996.
[6] Matheus C.J., Chan P.K.. Systems for knowledge discovery in databases [J]. IEEE Transactions on Knowledge and Data Engineering 1993, 6(6).
[7] Hideo Kasuga, Hiroaki Yamamoto, Masayuki Okamoto. Color Quantization Using the Fast K-Means Algorithm [J]. Systems and Computers in Japan 2000, 8(8).
[8] KAUFMAN L, ROUSSEEUW P J. Finding groups in data:an introduction to cluster analysis [J]. Finding Group in Date 1990, 7(02).
[9] BAR-OR R, VAN WOUDENBERG C. A novel gravity-based clustering method [D]. University of Colorado, Denver 2001.
[10] He ZY, Xu XF, Deng SC. Scalable algorithms for clustering large datasets with mixed type attributes [J]. International journal of intelligent systems 2005, 10(10).
[11] RAYMOND T Ng, HAN Jiawei. A method for clustering objects for spatial data mining [J]. IEEE Transactions on Knowledge and Data Engineering 2002, 14(05).
[12] ESTER M, KRIEGEL H P, SANDER J. A density-based algorithm for discovering clusters in large spatial databases with noise [A]. Portland, Oregon, USA 1996.
[13] BAR-OR R, VAN WOUDENBERG C. Gene expression analysis with a novel gravity-based clustering method [J]. Journal of Sanming University 2001, 34(02).
[14] ZHANG Zhen-yue, ZHA Hong-yuan. Principal Manifolds and Nonlinear Dimensionality Reduction via Tangent Space Alignment [J]. Journal of Shanghai University 2004, 4(4).
[15] CHANG Kecheng. Critical point theory and applications [M]. 上海:上海科学技术出版社 1986.
[16] 张军平. 流行学习及应用 [R]. 北京:中国科学院自动化研究所 2003.