Visual analytics for spatial clustering: Using a heuristic approach for guided exploration

IEEE Transactions on Visualization and Computer Graphics

Volumn 19, Issue 12, 2013, Pages 2179-2188

  Packer, Eli ^a   Bak, Peter ^a   Nikkila, Mikko ^b   Polishchuk, Valentin ^b   Ship, Harold J ^b  

^a IBM HAIFA RESEARCH LAB   (Israel)

^b UNIVERSITY OF HELSINKI   (Finland)

Author keywords

Heuristic based spatial clustering; iInteractive visual clustering; k order a (alpha) shapes

Indexed keywords

COHERENT FRAMEWORKS; HEURISTIC APPROACH; INTERACTIVE VISUALIZATIONS; K-ORDER A-(ALPHA)-SHAPES; SPATIAL CLUSTERING; VISUAL ANALYTICS; VISUAL CLUSTERING; VISUAL FEEDBACK;

COMPUTATIONAL GEOMETRY; DATA PROCESSING; HEURISTIC METHODS; VISUAL COMMUNICATION;

VISUALIZATION;

ALGORITHM; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER GRAPHICS; COMPUTER INTERFACE; COMPUTER SIMULATION; PROCEDURES; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THEORETICAL MODEL; ARTICLE; METHODOLOGY;

ALGORITHMS; COMPUTER GRAPHICS; COMPUTER SIMULATION; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MODELS, THEORETICAL; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; USER-COMPUTER INTERFACE;

EID: 84886651495     PISSN: 10772626     EISSN: None     Source Type: Journal    
DOI: 10.1109/TVCG.2013.224     Document Type: Article

References (38)

1. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan. Automatic subspace clustering of high dimensional data for data mining applications. In Proc. of the ACM SIGMOD, Int. Conf. Management of Data, volume 27, pages 94-105. ACM, 1998. (Pubitemid 128655960)
2. B. Alper, N. Riche, G. Ramos, and M. Czerwinski. Design study of linesets, a novel set visualization technique. IEEE Transactions on Visualization and Computer Graphics, 17(12):2259-2267, 2011.
3. D. Arthur and S. Vassilvitskii. k-means++: The advantages of careful seeding. In Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, pages 1027-1035. Society for Industrial and Applied Mathematics, 2007.
4. C. Brewer and M. Harrower. Color brewer 2.0. 2012.
5. E. T. Brown, J. Liu, C. E. Brodley, and R. Chang. Dis-function: Learning distance functions interactively. In Visual Analytics Science and Technology (VAST), 2012 IEEE Conference on, pages 83-92. IEEE, 2012.
6. K. Chen and L. Liu. A visual framework invites human into the clustering process. In Scientific and Statistical Database Management, 2003. 15th International Conference on, pages 97-106. IEEE, 2003.
7. K. Chen and L. Liu. Vista: Validating and refining clusters via visualization. Information Visualization, 3(4):257-270, 2004.
8. J. Choo, H. Lee, J. Kihm, and H. Park. ivisclassifier: An interactive visual analytics system for classification based on supervised dimension reduction. In Visual Analytics Science and Technology (VAST), 2010 IEEE Symposium on, pages 27-34. IEEE, 2010.
9. R. Cole, M. Sharir, and C. K. Yap. On k-hulls and related problems. SIJCOMP, 16(1):61-77, 1987.
10. C. Collins, G. Penn, and S. Carpendale. Bubble sets: Revealing set relations with isocontours over existing visualizations. IEEE Trans. on Visualization and Computer Graphics (Proc. of the IEEE Conf. on Information Visualization), 15(6), 2009.
11. M. de Berg, M. van Kreveld,M. Overmars, and O. Schwarzkopf. Computational Geometry: Algorithms and Applications. Springer-Verlag, Heidelberg, Germany, 2004.
12. L. Duan, L. Xu, F. Guo, J. Lee, and B. Yan. A local-density based spatial clustering algorithm with noise. Information Systems, 32(7):978-986, 2007. (Pubitemid 47017576)
13. H. Edelsbrunner. Alpha shapes-a survey. in Tessellations in the Sciences; Virtues, Techniques and Applications of Geometric TilingsVisualization and Data Analysis, 2010.
14. H. Edelsbrunner, D. G. Kirkpatrick, and R. Seidel. On the shape of a set of points in the plane. IEEE Trans. Inform. Theory IT-29, pages 551-559, 1983.
15. A. Endert, C. Han, D. Maiti, L. House, S. Leman, and C. North. Observation-level interaction with statistical models for visual analytics. In Visual Analytics Science and Technology (VAST), 2011 IEEE Conference on, pages 121-130. IEEE, 2011.
16. M. Ester, H. Kriegel, and X. Xu. Knowledge discovery in large spatial databases: Focusing techniques for efficient class identification. In Advances in Spatial Databases, pages 67-82. Springer, 1995.
17. V. Estivill-Castro and I. Lee. Amoeba: Hierarchical clustering based on spatial proximity using delaunay diagram. In Proceedings of the 9th International Symposium on Spatial Data Handling. Beijing, China. Citeseer, 2000.
18. S. Guha, R. Rastogi, and K. Shim. Cure: an efficient clustering algorithm for large databases. In ACM SIGMOD Record, volume 27, pages 73-84. ACM, 1998. (Pubitemid 128655958)
19. D. Guo. Coordinating computational and visual approaches for interactive feature selection and multivariate clustering. Information Visualization, 2(4):232-246, 2003.
20. D. Guo, D. Peuquet, and M. Gahegan. Opening the black box: interactive hierarchical clustering for multivariate spatial patterns. In Proceedings of the 10th ACM international symposium on Advances in geographic information systems, pages 131-136. ACM, 2002.
21. D. Guo, D. Peuquet, and M. Gahegan. Iceage: Interactive clustering and exploration of large and high-dimensional geodata. GeoInformatica, 7(3):229-253, 2003. (Pubitemid 37212162)
22. M. Halkidi, Y. Batistakis, and M. Vazirgiannis. On clustering validation techniques. Journal of Intelligent Information Systems, 17(2):107-145, 2001. (Pubitemid 34050790)
23. M. Han, J.; Kamber and A. Tung. Clustering methods in data mining: A survey. In Geographic Data Mining and Knowledge Discovery, pages 1-29. Taylor and Francis, 2001.
24. A. Hinneburg and D. Keim. An efficient approach to clustering in large multimedia databases with noise. pages 58-65, 1998.
25. HSL. Helsinki region transport-live vehicle api documentation. Website, 2011. http://developer.reittiopas.fi/pages/en/other-apis.php.
26. A. Jain, M. Murty, and P. Flynn. Data clustering: a review. ACM computing surveys (CSUR), 31(3):264-323, 1999.
27. T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu. An efficient k-means clustering algorithm: Analysis and implementation. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 24(7):881-892, 2002. (Pubitemid 34835464)
28. G. Karypis, E. Han, and V. Kumar. Chameleon: Hierarchical clustering using dynamic modeling. Computer, 32(8):68-75, 1999.
29. K. Koperski, J. Adhikary, and J. Han. Spatial data mining: progress and challenges survey paper. In Proc. ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, Canada, pages 1-10. Citeseer, 1996.
30. D. N. Krasnoshchekov and V. Polishchuk. Robust curve reconstruction with k-order alpha-shapes. In Shape Modeling International, pages 279-280, 2008. Full version: http://www.cs.helsinki.fi/u/polishch/pages/koas.pdf.
31. A. Lucieer and M.-J. Kraak. a-shapes for visualizing irregular-shaped class clusters in 3d feature space for classification of remotely sensed imagery. Proc. SPIE 5295, Visualization and Data Analysis, 16, 2004.
32. R. Maciejewski, R. Hafen, S. Rudolph, S. G. Larew, M. A. Mitchell,W. S. Cleveland, and D. S. Ebert. Forecasting hotspots-a predictive analytics approach. Visualization and Computer Graphics, IEEE Transactions on, 17(4):440-453, 2011.
33. L. Mu and R. Liu. A heuristic alpha-shape based clustering method for ranked radial pattern data. pages 621-630. Applied Geography, Vol. 31, No. 2., 2011.
34. R. Ng and J. Han. Efficient and effective clustering methods for spatial data mining. In Proceedings of the 20th International Conference on Very Large Data Bases, page 144. Morgan Kaufmann Pub., 1994.
35. J. Sander, M. Ester, H. Kriegel, and X. Xu. Density-based clustering in spatial databases: The algorithm gdbscan and its applications. Data Mining and Knowledge Discovery, 2(2):169-194, 1998.
36. R. Sibson. SLINK: an optimally efficient algorithm for the single-link cluster method. The Computer Journal, 16(1):30-34, 1973.
37. R. van de Weygaert, E. Platen, G. Vegter, B. Eldering, and N. Kruithof. Alpha shape topology of the cosmic web. In M. A. Mostafavi, editor, ISVD, pages 224-234. IEEE Computer Society, 2010.
38. J. Wang, W. Peng, M. O. Ward, and E. A. Rundensteiner. Interactive hierarchical dimension ordering, spacing and filtering for exploration of high dimensional datasets. In Information Visualization, 2003. INFOVIS 2003. IEEE Symposium on, pages 105-112. IEEE, 2003.