Related Paper for Association Rule Visualization/Exploration

  1. Michael Hahsler and Sudheer Chelluboina.
    Visualizing Association Rules: Introduction to the R-extension Package arulesViz.
    http://cran.r-project.org/web/packages/arulesViz/ PDF
  2. Tutut Herawan, Iwan Tri Riyadi Yanto and Mustafa Mat Deris.
    SMARViz: Soft Maximal Association Rules Visualization.
    IVIC 2009, LNCS 5857, pp. 664-674, 2009. PDF
  3. Carson Kai-Sang Leung, Pourang P. Irani, and Christopher L. Carmichael.
    FIsViz: A Frequent Itemset Visualizer.
    PAKDD 2008, LNAI 5012, pp. 644-652, 2008. PDF
  4. Baoqing Jiang, Chong Han, Xiaohua Hu.
    A finite ranked poset and its application in visualization of association rules.
    GrC 2008, 2008. PDF
  5. Julien Blanchard, Fabrice Guillet, and Henri Briand.
    Interactive visual exploration of association rules with rule-focusing methodology.
    Knowledge and Information Systems, Vol. 13, No. 1. (September 2007), pp. 43-75. PDF
  6. Olivier Couturier, Jose Rouillard and Vincent Chevrin.
    An Interactive Approach to Display Large Sets of Association Rules.
    Human Interface, Part I, HCII 2007, LNCS 4557, pp. 258-267, 2007. PDF
  7. Olivier Couturier, Tarek Hamrouni, Sadok B. Yahia, and Engelbert M. Nguifo.
    A scalable association rule visualization towards displaying large amounts of knowledge.
    Information Visualization, 2007. PDF
  8. Margherita Berardi, Annalisa Appice, Corrado Loglisci, and Pietro Leo.
    Supporting Visual Exploration of Discovered Association Rules Through Multi-Dimensional Scaling.
    ISMIS 2006, LNAI 4203, pp. 369-378, 2006. PDF
  9. Nan Jiang and Le Gruenwald.
    Research issues in data stream association rule mining.
    SIGMOD Record, Vol. 35, No. 1, Mar. 2006. PDF
  10. R. Marghoubi, A. Boulmakoul, and K. Zeitouni.
    The Use of the Galois lattice for the extraction and the visualization of the spatial association rules.
    Signal Processing and Information Technology, 2006. PDF
  11. Annalisa Appice and Paolo Buono.
    Analyzing Multi-level Spatial Association Rules Through a Graph-Based Visualization.
    IEA/AIE 2005, LNAI 3533, pp. 448-458, 2005. PDF
  12. Li Yang,
    Pruning and Visualizing Generalized Association Rules in Parallel Coordinates.
    Knowledge and Data Engineering, 2005. PDF
  13. Kesaraporn Techapichetvanich and Amitava Datta.
    VisAR : A New Technique for Visualizing Mined Association Rules.
    ADMA 2005, LNAI 3584, pp. 88-95, 2005. PDF
  14. Sadok B. Yahia and Engelbert M. Nguifo.
    Emulating a Cooperative Behavior in a Generic Association Rule Visualization Tool.
    ICTAI 2004. PDF
  15. Sadok B. Yahia and Engelbert M. Nguifo.
    Contextual generic association rules visualization using hierarchical fuzzy meta-rules.
    Proceedings of Fuzzy System 2004. PDF
  16. Dario Bruzzese and Paolo Buono.
    Combining visual techniques for Association Rules exploration.
    AVI '04, 2004. PDF
  17. Jane Hunter, Katya Falkovych, and Suzanne Little.
    Next Generation Search Interfaces - Interactive Data Exploration and Hypothesis Formulation.
    ECDL 2004, LNCS 3232, pp. 86-98, 2004. PDF
  18. Julien BLANCHARD, Fabrice GUILLET, and Henri BRIAND.
    Exploratory visualization for association rule rummaging.
    MDM/KDD'03, 2003. PDF
  19. Julien Blanchard, Fabrice Guillet, and Henri Briand.
    A user-driven and quality-oriented visualization for mining association rules.
    Proceedings of ICDM'03, 2003. PDF
  20. H. Hofmann, A. Siebes, and A. F. X. Wilhelm.
    Visualizing association rules with interactive mosaic plots.
    KDD, pages 227-235, 2000. PDF
  21. P. C. Wong, P. Whitney, and J. Thomas.
    Visualizing association rules for text mining.
    Proceedings of INFOVIS'99, 1999. PDF
  22. Jiawei Han and Yongjian Fu.
    Mining multiple-level association rules in large databases.
    Knowledge and Data Engineering, 1999. PDF
  23. Brian Lentt, Arun Swami, and Jennifer Widom.
    Clustering association rules.
    Data Engineering, 1997. 1997
ADDED:
  1. Kaidi Zhao, Bing Liu, Tom Tirpak, Andreas Schaller.
    "V-Miner: Using Enhanced Parallel Coordinates to Mine Product Design and Test Data."
    Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2004, full paper in Industrial Track), Seattle, Washington, USA, Aug 22-25, 2004. PDF
  2. Kaidi Zhao, Bing Liu, Thomas M. Tirpak, and Andeas Schaller.
    "Detecting Patterns of Change Using Enhanced Parallel Coordinates Visualization."
    Proceedings of the Third IEEE International Conference on Data Mining (ICDM'03), Melbourne, Florida, November 19-22, 2003. (Industrial track) PDF
  3. Kaidi Zhao, Bing Liu.
    "Visual Analysis of The Behavior of Discovered Rules."
    Workshop Notes in ACM SIGKDD-2001 Workshop on Visual Data Mining, San Francisco, CA; Aug 20, 2001 PDF
  4. Bing Liu, Kaidi Zhao, Jeffrey Benkler and Weimin Xiao.
    "Rule Interestingness Analysis Using OLAP Operations." Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2006, full paper), August 20 - 23, 2006, Philadelphia, USA. [ready soon]. PDF
  5. Kaidi Zhao, Bing Liu, Jeffrey Benkler and Weimin Xiao.
    "Opportunity Map: Identifying Causes of Failure - A Deployed Data Mining System."
    Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2006, industrial track full paper), August 20 - 23, 2006, Philadelphia, USA. PDF
MORE:
  1. Abdelaziz Berrado and George C. Runger.
    Using metarules to organize and group discovered association rules.
    Data Mining and Knowledge Discovery, Volume 14, Number 3, 409-431, 2007. PDF
  2. G. Ertek and A. Demiriz.
    A framework for visualizing association mining results.
    ISCIS, pages 593-602, 2006. PDF
  3. L. Yang.
    Visualizing frequent itemsets, association rules, and sequential patterns in parallel coordinates.
    ICCSA 2003, Lecture Notes in Computer Science, pages 21-30, 2003. PDF
  4. D. Bruzzese and C. Davino.
    Visual mining of association rules.
    Visual Data Mining, LNCS 4404, pp. 103-122, 2008. PDF
  5. Carson Kai-Sang Leung and Christopher L. Carmichael.
    FpViz: a visualizer for frequent pattern mining.
    VAKD '09, 2009. PDF
  6. Carson Kai-Sang Leung, Pourang P. Irani and Christopher L. Carmichael.
    WiFIsViz: Effective Visualization of Frequent Itemsets.
    ICDM '08, 2008. PDF
  7. Ioannis Kopanakis, Nikos Pelekis, Haralampos Karanikas, and Thomas Mavroudkis.
    Visual Techniques for the Interpretation of Data Mining Outcomes.
    PCI 2005, LNCS 3746, pp. 25-35, 2005. PDF
  8. Antony Unwin, Heike Hofmann and Klaus Bernt.
    The TwoKey Plot for Multiple Association Rules Control.
    PKDD 2001, LNAI 2168, pp. 472-483, 2001. PDF
  9. Kian-Huat, Ong Kok-Leong, Ong Wee-Keong, and Ng Ee-Peng Lim.
    CrystalClear: active Visualization of Association Rules.
    ICDM'02, 2002. PDF
  10. H Toivonen, M Klemettinen, and P Ronkainen.
    Pruning and grouping discovered association rules.
    Proceedings of KDD'95, 1995.
  11. Chris P. Rainsford and John F. Roddick.
    Visualisation of temporal interval association rules.
    IDEAL 2000, LNCS 1983, pp. 91-96, 2000. PDF
  12. Jianchao Han, Aijun An and Nick Cercone.
    CViz: An Interactive Visualization System for Rule Induction.
    Canadian AI 2000, LNAI 1822, pp. 214-226, 2000. PDF
  13. GUNJAN K. GUPTA , ALEXANDER STREHL AND JOYDEEP GHOSH.
    Distance based clustering of association rules.
    Proceedings of ANNIE '99, 1999. PDF
  14. Paolo Buono and Maria Francesca Costabile.
    Visualizing association rules in a framework for visual data mining.
    LNCS 3379, pp. 221-231, 2005. PDF
  15. R. J. Bayardo, Jr. and R. Agrawal.
    Mining the most interesting rules.
    Proceeding of KDD '99, 1999. PDF
  16. Irene Weber.
    On Pruning Strategies for Discovery of Generalized and Quantitative Association Rules.
    Proceedings of Knowledge Discovery and Data Mining Workshop, 1998.
  17. Devavrat Shahy, Laks V. S. Lakshmanan, Krithi Ramamrithamy, and S. Sudarshany.
    Interestingness and Pruning of Mined Patterns.
    Workshop Notes of the 1999 ACM SIGMOD Research Issues in Data Mining and Knowledge Discovery, 1999. PDF
  18. Cristina Davino, and Dario Bruzzese.
    Significant Knowledge Extraction from Association Rules.
    Electronic Proceedings of the International Conference Knowledge Extraction and Modeling Workshop, 2006. PDF
BOOK:
  1. C. H. Chen, A. Unwin, and W. Hardle, editors.
    Handbooks of Computational Statistics.
    Springer-Verlag, 2008.