Wavelet-based clustering method for managing spatial data in...

Data processing: database and file management or data structures – Database design – Data structure types

Reexamination Certificate

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C707S793000, C707S793000, C382S225000

Reexamination Certificate

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06882997

ABSTRACT:
The method termed WaveCluster for mining spatial data. WaveCluster considers spatial data as a multidimensional signals and applies wavelet transforms, a signal-processing technique, to convert the spatial data into the frequency domain. The wavelet transforms produce a transformed space where natural clusters in the data become more distinguishable. The method quantizes a feature space to determine cells of the feature space, assigns objects to the cells, applies a wavelet transform on the quantized feature space to obtain a transformed feature space, finds connected clusters in sub bands at different levels of the transformed feature space, assigns labels to the cells, creates a look-up table, and maps the objects to the clusters. The method can manage spatial data in a two-dimensional feature space. The method also is applicable to a feature space that is made up of an image taken by a satellite.

REFERENCES:
patent: 5465308 (1995-11-01), Hutcheson et al.
patent: 5647058 (1997-07-01), Agrawal et al.
patent: 5799301 (1998-08-01), Castelli et al.
patent: 5825909 (1998-10-01), Jang
patent: 6003029 (1999-12-01), Agrawal et al.
patent: 6032146 (2000-02-01), Chadha et al.
patent: 6105149 (2000-08-01), Bonissone et al.
patent: 6195459 (2001-02-01), Zhu
Li et al., A Survey on Wavelet Applications in Data Mining, Dec. 2002, SIGKDD Explorations, vol. 4, Issue 2—pp. 49-68.*
W. Wang, J. Yang, R. Muntz. “STING: A Statistical Information Grid Approach to Spatial Data Mining”. 1997. In Proceeding of the 23rd VLDB Conference pp. 186-195.*
G. Sheikholeslami and A. Zhang. “An Approach to Clustering Large Visual Databases Using Wavelet Transform”. Feb. 1997. In Proceedings of the SPIE Conference on Visual Data Exploration and Analysis IV, pp. 322-333.*
J.R. Smith and S. Chang. Transform Features for Texture Classification and Discrimination in Large Image Databawses. 1994. In Proceeding of the IEEE International Conference on Image Processing. pp. 407-411.*
M Ester, H. Kriegel, J. Sander, and X. Xu. “Clustering for Mining in Large Spatial Databases”. 1998. ScienTec Publishing. KI-Journal. Specail Issue on Data Mining.*
Michale L. Hilton, Bjorn D. Jawerth, and Ayan Sengupta. “Compressing Still and Moving Images with Wavelets.” Dec. 1994. Multimedia Systems. pp. 218-227.*
G. Sheikholeslami. S. Chatterjee, and A. Zhang. “WaveCluster. A Multi-Resolution Clustering Approach for Very Large Spatial Databases”. Aug. 1998. In Proceedings of the 24th VLDB conference. pp. 428-439.*
D. Allard and C. Fraley. Non parametric maximum likelihood estimation of features in spatial process using voronoi tesselation. Journal of the American Statistical Association, Dec. 1997.
Rakesh Agrawal, Johannes Gehrke, Dimitrios Gunopulos, and Phabhakar Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proceedings of the AGM SIGMOD Conference on Management of Data, pp. 94-105, Seattle, WA, 1998.
S. Byers and A.E. Raftery. Nearest neighbor clutter removal for estimating features in spatial point processes. Technical Report 295, Department of Statistics, University of Washington, 1995.
Special Issue on Content-Based Image Retrieval Systems, Editors V.N. Gudivada and V.V. Raghaven, IEEE Computer, 28(9), 1995.
M.Ester, H. Kriegel, J. Sander, and X.Xu. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In Proceedings of 2nd International Conference on KDD, 1996.
M. Ester, H. Kriegel, J. Sander, and X. Xu. Clustering for mining in large spatial databases. KI-Journal, 1998. Special Issue on Data Mining, ScienTec Publishing.
A.D. Gordon. Classification Methods for the Exploratory Analysis of Multivariate Data. Chapman and Hall, 1981.
Michael L. Hilton, Bjorn D. Jawerth, and Ayan Sengupta. Compressing Still and Moving Images with Wavelets. Multimedia Systems, 2(5):218-227, Dec. 1994.
Berthold Klaus Paul Horn. Robot Vision. The MIT Press, forth edition, 1988.
Charles E. Jacobs, Adam Finkelstein, and David H. Salesin. Fast multiresolution image querying. In SIGGRAPH 95, Los Angeles, California, Aug. 1995.
Donald E. Knuth. The Art of Computer Programming. Addison-Wessley, third edition, 1998.
S. Mallat. Multiresolution approximation and wavelet orthonormal bases of L®. Transactions of American Mathematical Society, 315:69-87, Sep. 1989.
S. Mallat. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11:674-693, Jul. 1989.
R.T. Ng and J. Han. Efficient and Effective Clustering Methods for Spatial Data Mining. In Proceedings of the 20th VLDB Conference, pp. 144-155, Santiago, Chile, 1994.
D. Nassimi and S. Sahni. Finding connected components and connected ones on a mesh-connected parallel computer. SIAM Journal on Computing, 9:744-757, 1980.
S. Openshaw. A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modelling. Transactions of the Institute of British Geographers, 2:459-472, 1977.
S. Openshaw and P. Taylor. Quantitative Geography: A British View, chapter The Modifiable Areal Unit Problem, pp. 60-69. London: Routledge, 1981.
E.J. Pauwels, P. Fiddelaers, and L. Van Gool. DOG-based unsupervised clustering for CBIR. In Proceedings of the 2nd International Conference on Visual Information Systems, pp. 13-20, San Diego, California, Dec. 1997.
J.R. Smith and S. Chang. Transform Features For Texture Classification and Discrimination in Large Image Databases. In Proceedings of the IEEE International Conference on Image Processing, pp. 407-411, 1994.
Robert Schalkoff. Pattern Recognition: Statistical, Structural and Neural Approaches. John Wiley & Sons, Inc., 1992.
G. Sheikholeslami. S. Chatterjee, and A. Zhang. WaveCluster. A Multi-Resolution Clustering Approach for Very Large Spatial Databases. In Proceedings of the 24th VLDB conference, pp. 428-439, New York City, Aug. 1998.
G. Strang and T. Nguyen. Wavelets and Filter Banks. Wellesley-Cambridge Press, 1996.
Y. Shilaoch and U. Vishkin. An O(logn) parallel connectivity algorithm. Journal of Algorithms, 3:57-67, 1982.
G. Sheikholeslami and A. Zhang. An Approach to Clustering Large Visual Databases Using Wavelet Transform. In Proceedings of the SPIE Conference on Visual Data Exploration and Analysis IV, pp. 322-333, San Jose, Feb. 1997.
G. Sheikholeslami, A. Zhang, and L. Brian. Geographical Data Classification and Retrieval. In Proceedings of the 5th ACM International Workshop on Geographical Information Systems, pp. 58-61, Las Vegas, Nevada, Nov. 1997.
Greet Uytterhoeven, Dirk Roose, and Adhemar Bultheel. Wavelet transforms using lifting scheme. Technical Report ITA-Wavelets Report WP 1.1, Katholieke Universiteit Leuven, Department of Computer Science, Belgium, Apr. 1997.
Wei Wang, Jiong Yang, and Richard Muntz. STING: A Statistical Information Grid Approach to Spatial Data Mining. In Proceedings of the 23rd VLDB Conference, pp. 186-195, Athens, Greece, 1997.
X. Xu, M. Ester, H. Kriegel, and J. Sander. A distribution-based clustering algorithm for mining in large spatial databases. In Proceedings of the 14th International Conference on Data Engineering, pp. 324-331, Orlando, FL, Feb. 1998.
D. Yu, S. Chatterjee, G. Sheikholeslami, and A. Zhang. Efficiently detecting arbitrary shaped clusters in very large datasets with high dimensions. Technical Report 98-8, State University of New York at Buffalo, Department of Computer Science and Engineering, Nov. 1998.
Mohamed Zait and Hammou Messatfa. A comparative study of clustering methods. Future Generation Computer Systems, 13:149-159, Nov. 1997.
Tian Zhang, Raghu Ramakrishnan, and Miron Livny. BIRCH: An Efficient Data Clustering Method for Very Large Databases. In Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, pp. 103-114.

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