An adaptive spatial clustering algorithm based on delaunay triangulation

In this paper, an adaptive spatial clustering algorithm based on Delaunay triangulation (ASCDT for short) is proposed. The ASCDT algorithm employs both statistical features of the edges of Delaunay triangulation and a novel spatial proximity definition based upon Delaunay triangulation to detect spatial clusters. Normally, this algorithm can automatically discover clusters of complicated shapes, and non-homogeneous densities in a spatial database, without the need to set parameters or prior knowledge. The user can also modify the parameter to fit with special applications. In addition, the algorithm is robust to noise. Experiments on both simulated and real-world spatial databases (i.e. an earthquake dataset in China) are utilized to demonstrate the effectiveness and advantages of the ASCDT algorithm.

[1]  Jean-Claude Thill,et al.  Local Indicators of Network-Constrained Clusters in Spatial Point Patterns , 2007 .

[2]  Ali S. Hadi,et al.  Finding Groups in Data: An Introduction to Chster Analysis , 1991 .

[3]  Hans-Peter Kriegel,et al.  A distribution-based clustering algorithm for mining in large spatial databases , 1998, Proceedings 14th International Conference on Data Engineering.

[4]  J. MacQueen.  Some methods for classification and analysis of multivariate observations , 1967 .

[5]  Jean-Claude Thill,et al.  Cross-scale analysis of cluster correspondence using different operational neighborhoods , 2008, J. Geogr. Syst..

[6]  Robert Haining,et al.  Spatial Data Analysis: Theory and Practice , 2003 .

[7]  Duoqian Miao,et al.  A graph-theoretical clustering method based on two rounds of minimum spanning trees , 2010, Pattern Recognit..

[8]  Victor J. D. Tsai,et al.  Delaunay Triangulations in TIN Creation: An Overview and a Linear-Time Algorithm , 1993, Int. J. Geogr. Inf. Sci..

[9]  Chia-Chen Yen,et al.  ANGEL: A New Effective and Efficient Hybrid Clustering Technique for Large Databases , 2007, PAKDD.

[10]  Ming-Syan Chen,et al.  Combining Partitional and Hierarchical Algorithms for Robust and Efficient Data Clustering with Cohesion Self-Merging , 2005, IEEE Trans. Knowl. Data Eng..

[11]  Aidong Zhang,et al.  WaveCluster: A Multi-Resolution Clustering Approach for Very Large Spatial Databases , 1998, VLDB.

[12]  Daniel A. Keim,et al.  An Efficient Approach to Clustering in Large Multimedia Databases with Noise , 1998, KDD.

[13]  H. L. Le Roy,et al.  Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability; Vol. IV , 1969 .

[14]  Dimitrios Gunopulos,et al.  Automatic subspace clustering of high dimensional data for data mining applications , 1998, SIGMOD '98.

[15]  Olga Sourina,et al.  Effective clustering and boundary detection algorithm based on Delaunay triangulation , 2008, Pattern Recognit. Lett..

[16]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[17]  Chenghu Zhou,et al.  Detecting feature from spatial point processes using Collective Nearest Neighbor , 2009, Comput. Environ. Urban Syst..

[18]  Sudipto Guha,et al.  CURE: an efficient clustering algorithm for large databases , 1998, SIGMOD '98.

[19]  Ickjai Lee,et al.  Multi-Level Clustering and its Visualization for Exploratory Spatial Analysis , 2002, GeoInformatica.

[20]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[21]  Jiawei Han,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[22]  Tian Zhang,et al.  BIRCH: an efficient data clustering method for very large databases , 1996, SIGMOD '96.

[23]  V. Estivill-Castro,et al.  Argument free clustering for large spatial point-data sets via boundary extraction from Delaunay Diagram , 2002 .

[24]  Charles T. Zahn,et al.  Graph-Theoretical Methods for Detecting and Describing Gestalt Clusters , 1971, IEEE Transactions on Computers.

[25]  Jiawei Han,et al.  Efficient and Effective Clustering Methods for Spatial Data Mining , 1994, VLDB.

[26]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[27]  Gabriella Schoier,et al.  A Clustering Method for Large Spatial Databases , 2004, ICCSA.

[28]  Jiong Yang,et al.  STING: A Statistical Information Grid Approach to Spatial Data Mining , 1997, VLDB.

[29]  Olga Sourina,et al.  Automatic clustering and boundary detection algorithm based on adaptive influence function , 2008, Pattern Recognit..

[30]  Hans-Peter Kriegel,et al.  Density-Based Clustering in Spatial Databases: The Algorithm GDBSCAN and Its Applications , 1998, Data Mining and Knowledge Discovery.

[31]  Hans-Peter Kriegel,et al.  OPTICS: ordering points to identify the clustering structure , 1999, SIGMOD '99.