Opening the black box: Interactive hierarchical clustering for multivariate spatial patterns

Proceedings of the ACM Workshop on Advances in Geographic Information Systems

Volumn , Issue , 2002, Pages 131-136

  Guo, Diansheng ^a   Peuquet, Donna ^a   Gahegan, Mark ^a  

^a PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Geographic Knowledge Discovery; Hierarchical Subspace Clustering; Spatial Ordering; Visualization and Interaction

Indexed keywords

ALGORITHMS; DATABASE SYSTEMS; VISUALIZATION;

MULTIVARIATE SPATIAL PATTERNS;

GEOGRAPHIC INFORMATION SYSTEMS;

EID: 0038305106     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (19)

1. Agrawal, R., Gehrke, J., Gunopulos, D. and Raghavan, P., Automatic subspace clustering of high dimensional data for data mining applications., ACM SIGMOD'98, Seattle, WA USA, 1998, pp. 94-105.
2. Ankerst, M., Breunig, M.M., Kriegel, H.-P. and Sander, J., OPTICS: Ordering Points To Identify the Clustering Structure., Proc. ACM SIGMOD'99, Philadelphia PA, 1999.
3. Baase, S. and Gelder, A. V., Computer Algorithms, 3rd edn., Addison-Wesley, 2000.
4. Cheng, C., Fu, A. and Zhang, Y., Entropy-based subspace clustering for mining numerical data., Proc. ACMS SIGKDD International Conference on Knowledge Discovery and Data Mining, 1999.
5. Duda, R.O., Hart, P.E. and Stork., D.G., Pattern classification and scene analysis, 2nd edn., Wiley, New York, 2000.
6. Ester, M., Kriegel, H.-P., Sander, J. and Xu, X., A density-based algorithm for discovering clusters in large spatial databases with noise., the 2nd International Conference on Knowledge Discovery and Data Mining, AAAI Press, Portland, Oregon, 1996.
7. Estivill-Castro, V. and Lee, I., Amoeba: Hierarchical Clustering Based On Spatial Proximity Using Delaunaty Diagram., 9th International Symposium on Spatial Data Handling, Beijing, China, 2000, pp. 7a.26 - 7a.41.
8. Gahegan, M., On the application of inductive machine learning tools to geographical analysis, Geographical Analysis, 32 (2000) 113-139.
9. Guibas, L. and Stolfi, J., Primitives for the Manipulation of General Subdivisions and the Computation of Voronoi Diagrams, ACT TOG, 4 (1985).
10. Harel, D. and Koren, Y., Clustering spatial data using random walks., Proc. of the seventh ACM SIGKDD international conference on knowledge discovery and data mining, San Francisco, California, 2001.
11. Hinneburg, A. and Keim, D.A., Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering., Proceedings of the 25th VLDB Conference, Edingburgh, Scotland, 1999.
12. Inselberg, A., The plane with parallel coordinates, The Visual Computer, 1 (1985) 69-97.
13. Jain, A.K. and Dubes, R.C., Algorithms for clustering data, Prentice Hall, Englewood Cliffs, NJ, 1988, 320 pp.
14. Miller, H.J. and Han, J., Geographic Data Mining and Knowledge Discovery: an overview. In H.J. Miller and J. Han (Eds.), Geographic Data Mining and Knowledge Discovery, Taylor & Francis, London and New York, 2001, pp. 3-32.
15. Openshaw, S., Charlton, M., Wymer, C. and Craft, A., A Mark 1 Geographical Analysis Machine for the automated analysis of point data sets, International Journal of Geographical Information Science, 1 (1987) 335-358.
16. Procopiuc, C.M., Jones, M., Agarwal, P.K. and Murali, T.M., A Monte Carlo Algorithm for Fast Projective Clustering., ACM SIGMOD'2002, Madison, Wisconsin, USA, 2002, pp. 418-427.
17. Slocum, T.A., Thematic cartography and visualization, Upper Saddle River, N.J.: Prentice Hall, 1999, 293 pp.
18. Tung, A.K.H., Hou, J. and Han, J., Spatial clustering in the presence of obstacles., The 17th International Conference on Data Engineering (ICDE'01), 2001.
19. Vandev, D. and G.Tsvetanova, Y., Perfect chains and Single Linkage Clustering Algorithm., Statistical Data Analysis, Proceedings SDA-95, 1995, pp. 99-107.