iVAS: An interactive visual analytic system for frequent set mining

Visual Analytics and Interactive Technologies: Data, Text and Web Mining Applications

Volumn , Issue , 2010, Pages 213-231

  Leung, Carson K S ^a   Carmichael, Christopher L ^a  

^a UNIVERSITY OF MANITOBA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 84898256597     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.4018/978-1-60960-102-7.ch013     Document Type: Chapter

References (34)

1. Aggarwal, C. C., & Yu, P. S. (2009). A survey of uncertain data algorithms and applications. IEEE Transactions on Knowledge and Data Engineering, 21(5), 609-623. doi:10.1109/TKDE.2008.190
2. Agrawal, R., Imieliński, T., & Swami, A. N. (1993). Mining association rules between sets of items in large databases. In P. Buneman & S. Jajodia (Eds.), Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data (pp. 207-216). New York: ACM.
3. Agrawal, R., & Srikank, R. (1994). Fast algorithms for mining association rules in large databases. In J.B. Bocca, M. Jarke, & C. Zaniolo (Eds.), Proceedings of the 20th International Conference on Very Large Data Bases (pp. 487-499). San Francisco, CA: Morgan Kaufmann.
4. Ahlberg, C. (1996). Spotfire: An information exploration environment. SIGMOD Record, 25(4), 25-29. doi:10.1145/245882.245893
5. Ankerst, M., Elsen, C., Ester, M., & Kriegel, H.-P. (1999). Visual classification: An interactive approach to decision tree construction. In U. Fayyad, S. Chaudhuri, & D. Madigan (Eds.), Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-99) (pp. 392-396). New York: ACM.
6. Bayardo, R. J. (1998). Efficiently mining long patterns from databases. In L.M. Haas & A. Tiwary (Eds.), Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data (pp. 85-93). New York: ACM.
7. Berchtold, S., Jagadish, H. V., & Ross, K. A. (1998). Independence diagrams: A technique for visual data mining. In R. Agrawal, P.E. Stolorz, & G. Piatetsky-Shapiro (Eds.), Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98) (pp. 139-143). Menlo Park, CA: AAAI Press.
8. Blanchard, J., Guillet, F., & Briand, H. (2007). Interactive visual exploration of association rules with rule-focusing methodology. Knowledge and Information Systems, 13(1), 43-75. doi:10.1007/s10115-006-0046-2
9. Cheng, H., & Han, J. (2009). Frequent itemsets and association rules. In Liu, L., & Özsu, M. T. (Eds.), Encyclopedia of Database Systems (pp. 1184-1187). New York: Springer.
10. Frawley, W. J., Piatetsky-Shapiro, G., & Matheus, C. J. (1991). Knowledge discovery in databases: An overview. In Piatetsky-Shapiro, G., & Frawley, W. J. (Eds.), Knowledge Discovery in Databases (pp. 1-30). Cambridge, MA: The MIT Press.
11. Gaber, M. M., Zaslavsky, A., & Krishnaswamy, S. (2005). Mining data streams: A review. SIGMOD Record, 34(2), 18-26. doi:10.1145/1083784.1083789
12. Grinstein, G., Plaisant, C., Laskowski, S., O'Connell, T., Scholtz, J., & Whiting, M. (2008). VAST 2008 Challenge: Introducing mini-challenges. In D. Ebert & T. Ertl (Eds.), Proceedings of the 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) (pp. 195-196). Piscataway, NJ: IEEE.
13. Han, J., & Cercone, N. (2000). AViz: A visualization system for discovering numeric association rules. In T. Terano, H. Liu, & A.L.P. Chen (Eds.), Proceedings of the Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (LNAI 1805, pp. 269-280). Berlin, Germany: Springer.
14. Han, J., Cheng, H., Xin, D., & Yan, X. (2007). Frequent pattern mining: Current status and future directions. Data Mining and Knowledge Discovery, 15(1), 55-86. doi:10.1007/s10618-006-0059-1
15. Keim, D. A., & Kriegel, H.-P. (1996). Visualization techniques for mining large databases: A comparison. IEEE Transactions on Knowledge and Data Engineering, 8(6), 923-938. doi:10.1109/69.553159
16. Keim, D. A., Mansmann, F., Schneidewind, J., & Ziegler, H. (2006). Challenges in visual data analysis. In E. Banissi, R.A. Burkhard, A. Ursyn, J.J. Zhang, M. Bannatyne, C. Maple, A.J. Cowell, G.Y. Tian, & M. Hou (Eds.), Proceedings of the 10th IEEE International Conference on Information Visualization (pp. 9-16). Los Alamitos, CA: IEEE Computer Society.
17. Keim, D. A., Mansmann, F., Stoffel, A., & Ziegler, H. (2009). Visual analytics. In Liu, L., & Özsu, M. T. (Eds.), Encyclopedia of Database Systems (pp. 3341-3346). New York: Springer.
18. Koren, Y., & Harel, D. (2003) A two-way visualization method for clustered data. In L. Getoor, T.E. Senator, P. Domingos, & C. Faloutsos (Eds.), Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-03) (pp. 589-594). New York: ACM.
19. Lakshmanan, L. V. S., Leung, C. K.-S., & Ng, R. T. (2003). Efficient dynamic mining of constrained frequent sets. ACM Transactions on Database Systems, 28(4), 337-389. doi:10.1145/958942.958944
20. Leung, C. K.-S. (2009). Constraint-based association rule mining. In Wang, J. (Ed.), Encyclopedia of Data Warehousing and Mining (2nd ed., pp. 307-312). Hershey, PA: IGI Global.
21. Leung, C. K.-S., & Carmichael, C. L. (2009). FpViz: A visualizer for frequent pattern mining. In K. Puolamäki (Ed.), Proceedings of the ACM SIGKDD Workshop on Visual Analytics and Knowledge Discovery: Integrating Automated Analysis with Interactive Exploration (pp. 30-39). New York: ACM.
22. Leung, C. K.-S., & Hao, B. (2009). Mining of frequent itemsets from streams of uncertain data. In Y. Ioannidis, D. Lee, & R. Ng (Eds.), Proceedings of the 25th IEEE International Conference on Data Engineering (pp. 1663-1670). Los Alamitos, CA: IEEE Computer Society.
23. Leung, C. K.-S., Irani, P. P., & Carmichael, C. L. (2008a). FIsViz: A frequent itemset visualizer. In T. Washio, E. Suzuki, K.M. Ting, & A. Inokuchi (Eds.), Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (LNAI 5012, pp. 644-652). Berlin, Germany: Springer.
24. Leung, C. K.-S., Irani, P. P., & Carmichael, C. L. (2008b). WiFIsViz: Effective visualization of frequent itemsets. In F. Giannotti, D. Gunopulos, F. Turini, C. Zaniolo, N. Ramakrishnan, & X. Wu (Eds.), Proceedings of the Eighth IEEE International Conference on Data Mining (pp. 875-880). Los Alamitos, CA: IEEE Computer Society.
25. Leung, C. K.-S., Khan, Q. I., Li, Z., & Hoque, T. (2007). CanTree: A canonical-order tree for incremental frequent-pattern mining. Knowledge and Information Systems, 11(3), 287-311. doi:10.1007/s10115-006-0032-8
26. Leung, C. K.-S., Mateo, M. A. F., & Brajczuk, D. A. (2008c). A tree-based approach for frequent pattern mining from uncertain data. In T. Washio, E. Suzuki, K.M. Ting, & A. Inokuchi (Eds.), Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (LNAI 5012, pp. 653-661). Berlin, Germany: Springer.
27. Liu, B. (2009). Classification by association rule analysis. In Liu, L., & Özsu, M. T. (Eds.), Encyclopedia of Database Systems (pp. 335-340). New York: Springer.
28. Munzer, T., Kong, Q., Ng, R. T., Lee, J., Klawe, J., Radulovic, D., & Leung, C. K. (2005). Visual mining of power sets with large alphabets. Technical report UBC CS TR-2005-25. Vancouver, BC, Canada: Department of Computer Science, The University of British Columbia.
29. Pasquier, N., Bastide, Y., Taouil, R., & Lakhal, L. (1999). Discovering frequent closed itemsets for association rules. In C. Beeri & P. Buneman (Eds.), Proceedings of the Seventh International Conference on Database Theory (LNCS 1540, pp. 398-416). Berlin, Germany: Springer.
30. Pei, J., Han, J., Mortazavi-Asl, B., & Zhu, H. (2000). Mining access patterns efficiently from web logs. In T. Terano, H. Liu, & A.L.P. Chen (Eds.), Proceedings of the Fourth Pacific-Asia Conference on Knowledge Discovery and Data Mining (LNAI 1805, pp. 396-407). Berlin, Germany: Springer.
31. Schreck, T., Bernard, J., Tekušová, T., & Kohlhammer, J. (2008). Visual cluster analysis of trajectory data with interactive Kohonen Maps. In D. Ebert & T. Ertl (Eds.), Proceedings of the 2008 IEEE Symposium on Visual Analytics Science and Technology (VAST) (pp. 3-10). Piscataway, NJ: IEEE.
32. Stolte, C., Tang, D., & Hanrahan, P. (2002). Query, analysis, and visualization of hierarchically structured data using Polaris. In D. Hand, D. Keim, & R. Ng (Eds.), Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-02) (pp. 112-122). New York: ACM.
33. Thomas, J. J., & Cook, K. A. (Eds.). (2005). Illuminating the Path: The Research and Development Agenda for Visual Analytics. Los Alamitos, CA: IEEE Computer Society.
34. Yang, L. (2005). Pruning and visualizing generalized association rules in parallel coordinates. IEEE Transactions on Knowledge and Data Engineering, 17(1), 60-70. doi:10.1109/TKDE.2005.14