Opportunity map: a visualization framework for fast identification of actionable knowledge

ABSTRACT

Data mining techniques frequently find a large number of patterns or rules, which make it very difficult for a human analyst to interpret the results and to find the truly interesting and actionable rules. Due to the subjective nature of "interestingness", human involvement in the analysis process is crucial. In this paper, we propose a novel visual data mining framework for the purpose of identifying actionable knowledge quickly and easily from discovered rules and data. This framework is called the Opportunity Map. It is inspired by some interesting ideas from Quality Engineering, in particular Quality Function Deployment (QFD) and the House of Quality. It associates summarized data or discovered rules with the application objective using an interactive matrix, which enables the user to quickly identify where the opportunities are. The proposed system can be used to visually analyze discovered rules, and other statistical properties of the data. The user can also interactively group actionable attributes and values, and see how they affect the targets of interest. Combined with drill-down and comparative analysis, the user can analyze rules and data at different levels of detail. The proposed visualization framework thus represents a systematic and yet flexible method of rule analysis. Applications of the system to large-scale data sets from our industrial partner have yielded promising results.

REFERENCES

• 1: Adomavicius, G. and Tuzhilin, A. Discovery of actionable patterns in databases: the action hierarchy approach. KDD-97, 1997.
• 2: Rakesh Agrawal , Ramakrishnan Srikant, Fast Algorithms for Mining Association Rules in Large Databases, Proceedings of the 20th International Conference on Very Large Data Bases, p.487-499, September 12-15, 1994
• 3: Roberto J. Bayardo, Jr. , Rakesh Agrawal, Mining the most interesting rules, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.145-154, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312219]
• 4: Blanchard J, Guillet F, Briand H. Exploratory Visualization for Association Rule Rummaging. KDD-03 Workshop on Multimedia Data Mining (MDM-03), 2003.
• 5: Bruzzese D., Davino C. Visual Post-Analysis of Association Rules. ECML/PKDD02 VDM Workshop, 2002.
• 6: Cohen L. Quality Function Deployment. Prentice Hall, 1995.
• 7: Jianchao Han , Nick Cercone, RuleViz: a model for visualizing knowledge discovery process, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p.244-253, August 20-23, 2000, Boston, Massachusetts, United States [doi>10.1145/347090.347139]
• 8: Han, J., Fu, Y., Wang, W., Koperski, K. and Zaiane, O. DMQL: a data mining query language for relational databases. SIGMOD Workshop on DMKD, 1996.
• 9: Heike Hofmann , Arno P. J. M. Siebes , Adalbert F. X. Wilhelm, Visualizing association rules with interactive mosaic plots, Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, p.227-235, August 20-23, 2000, Boston, Massachusetts, United States [doi>10.1145/347090.347133]
• 10: Robert J. Hilderman , Howard J. Hamilton, Evaluation of Interestingness Measures for Ranking Discovered Knowledge, Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining, p.247-259, April 16-18, 2001
• 11: Szymon Jaroszewicz , Dan A. Simovici, Interestingness of frequent itemsets using Bayesian networks as background knowledge, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014074]
• 12: Jorge A., Pocas J., Azevedo P. Post-processing environment for browsing large sets of association rules. PKDD-02 VDM Workshop, 2002.
• 13: Daniel A. Keim, Information Visualization and Visual Data Mining, IEEE Transactions on Visualization and Computer Graphics, v.8 n.1, p.1-8, January 2002 [doi>10.1109/2945.981847 ]
• 14: Liu, B., Hsu, W., and Chen, S. Using general impressions to analyze discovered classification rules. KDD-97, 1997.
• 15: Liu B., Hsu W, Ma Y. Integrating Classification and Association Rule Mining. KDD-98, 1998.
• 16: Bing Liu , Wynne Hsu , Yiming Ma, Mining association rules with multiple minimum supports, Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, p.337-341, August 15-18, 1999, San Diego, California, United States [doi>10.1145/312129.312274]
• 17: Bing Liu , Wynne Hsu , Yiming Ma, Identifying non-actionable association rules, Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining, p.329-334, August 26-29, 2001, San Francisco, California [doi>10.1145/502512.502560]
• 18: Ma S., Hellerstein J. Ordering Categorical Data to Improve Visualization. INFOVIS-99, 1999.
• 19: Rosa Meo , Giuseppe Psaila , Stefano Ceri, A New SQL-like Operator for Mining Association Rules, Proceedings of the 22th International Conference on Very Large Data Bases, p.122-133, September 03-06, 1996
• 20: Ong K-H, Ong K-L, Ng W-K, Lim E-P. CrystalClear: Active Visualization of Association Rules. ICDM-02 Workshop on Active Mining (AM-02), 2002.
• 21: Padmanabhan, B. and Tuzhilin, A. A belief-driven method for discovering unexpected patterns. KDD-98, 1998.
• 22: Piatesky-Shapiro, G., and Matheus, C. The interestingness of deviations. KDD-94, 1994.
• 23: J. Ross Quinlan, C4.5: programs for machine learning, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1993
• 24: Rasmussen M, Karypis G. gCLUTO - an interactive clustering, visualization, and analysis system. Technical Report # 04-021, University of Minnesota, 2004.
• 25: Shah, D., Lakshmanan, L.V.S, Ramamritham, K., and Sudarshan, S., 1999. Interestingness and Pruning of Mined Patterns. DMKD-99, 1999.
• 26: Avi Silberschatz , Alexander Tuzhilin, What Makes Patterns Interesting in Knowledge Discovery Systems, IEEE Transactions on Knowledge and Data Engineering, v.8 n.6, p.970-974, December 1996 [doi>10.1109/69.553165 ]
• 27: Suzuki, E. Autonomous discovery of reliable exception rules. KDD-97, 1997.
• 28: Terninko J. Step-by-Step QFD: Customer-Driven Product Design. Saint Lucie Press. 1997.
• 29: Alexander Tuzhilin , Gediminas Adomavicius, Handling very large numbers of association rules in the analysis of microarray data, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775104]
• 30: Alexander Tuzhilin , Bing Liu, Querying multiple sets of discovered rules, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, Canada [doi>10.1145/775047.775055]
• 31: Tomasz Imieliński , Aashu Virmani, MSQL: A Query Language for Database Mining, Data Mining and Knowledge Discovery, v.3 n.4, p.373-408, December 1999 [doi>10.1023/A:1009816913055 ]
• 32: Pak Chung Wong , Paul Whitney , Jim Thomas, Visualizing Association Rules for Text Mining, Proceedings of the 1999 IEEE Symposium on Information Visualization, p.120, October 24-29, 1999
• 33: Zhao K. Liu B. Visual Analysis of the Behavior of Discovered Rules. KDD-2001 Workshop on Visual DM.
• 34: Kaidi Zhao , Bing Liu , Thomas M. Tirpak , Andreas Schaller, V-Miner: using enhanced parallel coordinates to mine product design and test data, Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining, August 22-25, 2004, Seattle, WA, USA [doi>10.1145/1014052.1014108]

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.2 DATABASE MANAGEMENT
      H.2.8 Database applications
          Subjects: Data mining

Additional Classification:
  I. Computing Methodologies
  I.3 COMPUTER GRAPHICS
      I.3.m Miscellaneous

General Terms:
Design, Human Factors, Management

Keywords:
actionable rules, information visualization, interactive data exploration, patterns, visual data mining