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Interactive visual exploration of association rules with rule-focusing methodology

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Abstract

On account of the enormous amounts of rules that can be produced by data mining algorithms, knowledge post-processing is a difficult stage in an association rule discovery process. In order to find relevant knowledge for decision making, the user (a decision maker specialized in the data studied) needs to rummage through the rules. To assist him/her in this task, we here propose the rule-focusing methodology, an interactive methodology for the visual post-processing of association rules. It allows the user to explore large sets of rules freely by focusing his/her attention on limited subsets. This new approach relies on rule interestingness measures, on a visual representation, and on interactive navigation among the rules. We have implemented the rule-focusing methodology in a prototype system called ARVis. It exploits the user's focus to guide the generation of the rules by means of a specific constraint-based rule-mining algorithm.

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References

  1. Aggarwal CC (2002) Towards effective and interpretable data mining by visual interaction. ACM SIGKDD Explor 3(2):11–22

    Article  MathSciNet  Google Scholar 

  2. Agrawal R, Imielinski T, Swami A (1993) Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on management of data, Washington, DC, ACM Press, New York, pp 207–216

    Chapter  Google Scholar 

  3. Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In: Booka JB, Jarke M, Zaniolo (eds) Proceedings of the 20th international conference on very large data bases (VLDB), Santiago de Chile, Chile, Morgan Kaufmann, San Fransisco, pp 487–499

    Google Scholar 

  4. Agrawal R, Arning A, Bollinger T, Mehta M, Shafer J, Srikant R (1996) The Quest data mining system. In: Proceedings of the 2nd ACM SIGKDD international conference on knowledge discovery and data mining. AAAI Press, Menlo Park, pp 244–249, www.almaden.ibm.com/software/quest/

    Google Scholar 

  5. Ammoura A, Zaiane OR, Ji Y (2001) Immersed visual data mining: walking the walk. In: BNCOD 18: Proceedings of the 18th British national conference on databases, Chilton, UK. Springer-Verlag, Berlin Heidelberg New York, pp 202–218

    Google Scholar 

  6. Andrews K (1995) Visualising cyberspace: information visualisation in the Harmony internet browser. In: Proceedings of the 1995 IEEE symposium on information visualization, Atlanta, GA. IEEE Computer Society, Washington, DC, pp 97–104

    Google Scholar 

  7. Baird JC (1970) Psychophysical analysis of visual space. Pergamon Press, UK

    Google Scholar 

  8. Barthelemy J-P. Mullet E (1992) A model of selection by aspects. Acta Psychol 79(1):1–19

    Article  Google Scholar 

  9. Bayardo RJ, Jr, Agrawal R (1999) Mining the most interesting rules. In: Proceedings of the 5th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, ACM Press, New York, pp 145–154

    Chapter  Google Scholar 

  10. Bertin J (1967) Sémiologie Graphique (Gauthier-Villars, English translation by Berg W. J. as Semiology of Graphics, 1983). University of Wisconsin Press, Wisconsin

    Google Scholar 

  11. Bhandari I (1994) Attribute focusing: machine-assisted knowledge discovery applied to software production process control. Knowl Acquisit 6(3):271–294

    Article  MathSciNet  Google Scholar 

  12. Bisdorff R (ed) (2003) Proceeding of the mini-EURO conference on human centered processes HCP'2003, Luxemberg, University of Luxembourg, Luxembourg

  13. Blake CL, Merz CJ (1998) UCI repository of machine learning databases. Department of Information and Computer Science, University of California, Irvine, CA. www.ics.uci.edu/mlearn/MLRepository.html

    Google Scholar 

  14. Blanchard J, Kuntz P, Guillet F, Gras R (2003) Implication intensity: from the basic statistical definition to the entropic version. In: Bozdogan H (ed) Statistical data mining and knowledge discovery. Chapman & Hall/CRC Press, Boca Raton, pp 473–485

    Google Scholar 

  15. Blanchard J (2005) A visualization system for interactive mining, assessment, and exploration of association rules. Ph.D. thesis, University of Nantes (in French)

  16. Blanchard J, Guillet F, Briand H, Gras R (2005) Assessing rule interestingness with a probabilistic measure of deviation from equilibrium. In: Proceedings of the 11th international symposium on applied stochastic models and data analysis ASMDA-2005, ENST, pp 191–200

  17. Blanchard J., Guillet F, Briand H, Gras R (2005) Using information-theoretic measures to assess association rule interestingness. In: Proceedings of the 5th IEEE international conference on data mining ICDM'05, New Orleans, LA. IEEE Computer Society, Washington, DC, pp 66–73

    Chapter  Google Scholar 

  18. Bonchi F, Giannotti F, Mazzanti A, Pedreschi D (2005) Efficient breadth-first mining of frequent pattern with monotone constraints. Knowl Inform Syst 8(2):131–153

    Article  Google Scholar 

  19. Botta M, Boulicaut JF, Masson C, Meo R (2002) A comparison between query languages for the extraction of association rules. In: Proceedings of the 4th international conference on data warehousing and knowlege discovery (DaWaK 2002), Aix-en-Provence, France, Lecture notes in computer science, vol 2454, Springer-Verlag, Berlin Heidelberg New York

    Google Scholar 

  20. Brachman, JR, Anand T (1996) The process of knowledge discovery in databases: a human-centered approach. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI/MIT Press, Melno Park, CA, pp 37–58

    Google Scholar 

  21. Braga D, Campi A, Klemettinen M, Lanzi PL (2002) Mining association rules from XML Data. In: Proceedings of the 4th international conference on data warehousing and knowlege discovery (DaWaK 2002), Aix-en-Provence, France, Lecture notes in computer science, vol 2454, Springer-Verlag, Berlin Heidelberg New York

    Google Scholar 

  22. Brin S, Motwani R, Ullman JD, Tsur S (1997) Dynamic itemset counting and implication rules for market basket data. SIGMOD Rec 26(2):255–264

    Article  Google Scholar 

  23. Brunk C, Kelly J, Kohavi R (1997) MineSet: an integrated system for data mining. In: Proceedings of the 3rd ACM SIGKDD international conference on knowledge discovery and data mining, Washington DC. AAAI Press, Melno Park, pp 135–138

    Google Scholar 

  24. Card SK, Mackinlay JD, Schneiderman B (eds) (1999) Readings in information visualization: using vision to think. Morgan Kaufmann, San Fransisco

    Google Scholar 

  25. Carswell CM, Frankenberger S, Bernhard D (1991) Graphing in depth: perspectives on the use of three-dimensional graphs to represent lower-dimensional data. Behav Inform Technol 10(6):459–474

    Article  Google Scholar 

  26. Chen C (2004) Information visualization: beyond the horizon. Springer-Verlag, Berlin Heidelberg New York

    Google Scholar 

  27. Cleveland WS, McGill R (1984) Graphical perception: theory, experimentation, and application to the development of graphical methods. J Am Stat Assoc 79(387): 531–554

    Article  MathSciNet  Google Scholar 

  28. Cockburn A, McKenzie B (2001) 3D or not 3D? Evaluating the effect of the third dimension in a document management system. In: CHI'01: Proceedings of the SIGCHI conference on human factors in computing systems, Pittsburgh, PA. ACM Press, New York, pp 434–441

    Chapter  Google Scholar 

  29. Fayyad UM, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery: an overview. In: Fayyad UM, Piatetsky-Shapiro G, Smyth P, Uthurusamy R (eds) Advances in knowledge discovery and data mining. AAAI/MIT Press, Melno Park, pp 1–34

    Google Scholar 

  30. Fayyad UM, Grinstein GG, Wierse A (2001) Information visualization in data mining and knowledge discovery. Morgan Kaufmann, San Fransisco

    Google Scholar 

  31. Freitas AA (1998) On objective measures of rule surprisingness. In: Proceedings of the 2nd European conference on principles of data mining and knowledge discovery (PKDD'98), Nantes, France. Lecture notes in artificial intelligence, vol 1510, Springer-Verlag, Berlin Heidelberg New York

    Google Scholar 

  32. Fukuda T, Morimoto Y, Morishita S, Tokuyama T (2001) Data mining with optimized two-dimensional association rules. ACM Trans Database Syst 26(2):179–213

    Article  Google Scholar 

  33. Fule P, Roddick JF (2004) Experiences in building a tool for navigating association rule result sets. In: Hogan J, Montague P, Purvis M, Steketee C (eds) CRPIT'04: Proceedings of the second Australasian workshop on data mining and web intelligence, Darlinghurst, Australia. Australian Computer Society, Sydney, pp 103–108

    Google Scholar 

  34. Goethals B, Van den Bussche J. (2000) On Supporting interactive association rule mining. In: Proceedings of the 2nd international conference on data warehousing and knowledge discovery (DaWaK2000), London, UK, Lecture notes in computer science, vol 1874, pp 307–316. Springer-Verlag, Berlin Heidelberg New York

    Chapter  Google Scholar 

  35. Grahne G, Lakshmanan LVS, Wang X (2000) Efficient mining of constrained correlated sets. In: Proceedings of the sixteenth international conference on data engineering (ICDE), San Diego, CA, 28 February to 3 March 2000. IEEE Computer Society, Washington, DC, pp 512–521

    Google Scholar 

  36. Gras R (1996) L'implication statistique: nouvelle méthode exploratoire de données. La Pensée Sauvage Editions (in French)

  37. Guillaume S, Guillet F, Philippe J (1998) Improving the discovery of association rules with intensity of implication. In: Proceedings of the 2nd European conference on principles of data mining and knowledge discovery (PKDD'98), Nantes, France. Lecture notes in artificial intelligence, vol 1510, Springer-Verlag, Berlin Heidelberg New York

    Google Scholar 

  38. Han J, Fu Y, Wang W, Koperski K, Zaiane O (1996) DMQL: a data mining query language for relational databases. Paper presented at the 1996 SIGMOD workshop on research issues on data mining and knowledge discovery (DMKD), Montreal, Canada

  39. Han J, Chiang JY, Chee S, Chen J, Chen Q, Cheng S, Gong W, Kamber M, Koperski K, Liu G, Lu Y, Stefanovic N, Winstone L, Xia B, Zaiane OR, Zhang S, Zhu H (1997) DBMiner: a system for data mining in relational databases and data warehouses. In: Proceedings of CASCON'97: Meeting of minds, Toronto, Ontario, pp 249–260

  40. Han J, Pei J, Yin Y (2000) Mining frequent patterns without candidate generation. In: Proceedings of the ACM SIGMOD international conference on management of data, Dallas, Texas, ACM Press, New York, pp 1–12

    Chapter  Google Scholar 

  41. Han J, An A, Cercone N (2000) CViz: an interactive visualization system for rule induction. In: AI'00: Proceedings of the 13th Biennial conference of the Canadian Society on Computational Studies of Intelligence, Montreal, Quebec, Canada, Springer-Verlag, Berlin Heidelberg New York, pp 214–226

    Google Scholar 

  42. Han J, Hu X, Cercone N (2003) A visualization model of interactive knowledge discovery systems and its implementations. Inform Visual 2(2):105–125

    Article  Google Scholar 

  43. Hao MC, Dayal U, Hsu M, Sprenger T, Gross MH (2001) Visualization of directed associations in e-commerce transaction data. In: Proceedings of VisSym 2001, Ascona, Switzerland. Springer-Verlag, Berlin Heidelberg New York, pp 185–192

    Google Scholar 

  44. Hipp J, Güntzer U, Nakhaeizadeh G (2000) Algorithms for association rule mining: a general survey and comparison. SIGKDD Explor 2(1):58–64

    Article  Google Scholar 

  45. Hipp J, Güntzer U (2002) Is pushing constraints deeply into the mining algorithms really what we want? An alternative approach for association rule mining. SIGKDD Explor 4(1):50–55

    Article  Google Scholar 

  46. Hofmann H, Siebes AP, Wilhelm AF (2000) Visualizing association rules with interactive mosaic plots. In: Proceedings of the 6th ACM SIGKDD international conference on knowledge discovery and data mining, Boston, MA. ACM Press, New York, pp 227–235

    Chapter  Google Scholar 

  47. Hofmann H, Wilhelm A (2001) Visual comparison of association rules. Comput Stat 16(3):399–415

    Article  MATH  MathSciNet  Google Scholar 

  48. Holland JH, Holyoak KJ, Nisbett RE, Thagard PR (1986) Induction: processes of inference, learning and discovery. MIT Press, Cambridge, MA

    Google Scholar 

  49. Hussain F, Liu H, Suzuki E, Lu H (2000) Exception rule mining with a relative interestingness measure. In: Proceedings of the 4th Pacific-Asia conference on knowledge discovery and data mining (PAKDD2000), Kyoto, Japan, Lecture notes in computer science, vol 1805, pp 86–97. Springer-Verlag, Berlin Heidelberg New York

    Google Scholar 

  50. IBM (2006) DB2 intelligent miner visualization. www.ibm.com/software/data/iminer/visu- alization/index.html

  51. Imielinski T, Mannila H (1996) A database perspective on knowledge discovery. Commun ACM 39(11):58–64

    Article  Google Scholar 

  52. Imielinski T, Virmani A (1999) MSQL: a query language for database mining. Data Min Knowl Discov 3(4):373–408

    Article  Google Scholar 

  53. Jeudy B, Boulicaut J-F (2002) Optimization of association rule mining queries. Intell Data Anal 6(4)341–357

    MATH  Google Scholar 

  54. Keim DA (2002) Information visualization and visual data mining. IEEE Trans Visual Comput Graphics 8(1):1–8

    Article  Google Scholar 

  55. Klemettinen M, Mannila H, Ronkainen P, Toivonen H, Verkamo AI (1994) Finding interesting rules from large sets of discovered association rules. In: Proceedings of the 3rd international conference on information and knowledge management (CIKM), Gaithersburg, Maryland, ACM Press, New York, pp 401–407

    Chapter  Google Scholar 

  56. Kopanakis I, Theodoulidis B (2001) Visual data mining and modeling techniques. Paper presented at the KDD-2001 workshop on visual data mining, San Francisco, CA

  57. Kuntz P, Guillet F, Lehn R, Briand H (2000) A user-driven process for mining association rules. In: Proceedings of the 4th European conference on principles of data mining and knowledge discovery (PKDD-2000), Lyon, France, Springer-Verlag, Berlin Heidelberg New York, pp 483–489

    Google Scholar 

  58. Liu B, Hsu W, Wang K, Chen S (1999) Visually aided exploration of interesting association rules. In: Proceedings of the 3rd Pacific-Asia conference on knowledge discovery and data mining (PAKDD1999), Beijing, China, Lectures notes in artificial intelligence, vol 1574, pp 380–389. Springer-Verlag, Berlin Heidelberg New York

    Google Scholar 

  59. Liu B, Hsu W, Chen S, Ma Y (2000) Analyzing the subjective interestingness of association rules. IEEE Intell Syst 15(5):47–55

    Article  Google Scholar 

  60. Loevinger J (1947) A systematic approach to the construction and evaluation of tests of ability. Psychol Monogr 61(4)

  61. Ma Y, Liu B, Wong CK (2000) Web for data mining: organizing and interpreting the discovered rules using the Web. SIGKDD Explor 2(1):16–23

    Article  Google Scholar 

  62. McEachren AM (1995) How maps work: representation, visualization, and design. The Guilford Press, New York

    Google Scholar 

  63. Meo R, Psaila G, Ceri S (1998) An extension to SQL for mining association rules. Data Min Knowl Discov 2(2):195–224

    Article  Google Scholar 

  64. Montgomery H (1983) Decision rules and the search for a dominance structure: towards a process model of decision making. In: Humphreys PC, Svenson O, Vari A (eds) Analysing and aiding decision processes. North Holland, Amsterdam, pp 343–369

    Google Scholar 

  65. Ng RT, Lakshmanan LVS, Han J, Pang A (1998) Exploratory mining and pruning optimizations of constrained associations rules. In: Proceedings of the 1998 ACM SIGMOD international conference on management of data, Seattle, ACM Press, New York, pp 13–24

    Chapter  Google Scholar 

  66. Ordonez C, Ezquerra N, Santana CA (2006) Constraining and summarizing association rules in medical data. Knowl Inform Syst 9(3):1–2

    Article  Google Scholar 

  67. Padmanabhan B, Tuzhilin A (1999) Unexpectedness as a measure of interestingness in knowledge discovery. Decision Support Syst 27(3):303–318

    Article  Google Scholar 

  68. Piatetsky-Shapiro G (1991) Discovery, analysis, and presentation of strong rules. In: Piatetsky-Shapiro G, Frawley WJ (eds) Knowledge discovery in databases. AAAI/MIT Press, Melno Park, pp 229–248

    Google Scholar 

  69. Rainsford CP, Roddick JF (2000) Visualisation of temporal interval association rules. In: Proceedings of the 2nd international conference on intelligent data engineering and automated learning (IDEAL 2000), Shatin, Hong Kong, Springer-Verlag, Berlin Heidelberg New York, pp 91–96

    Google Scholar 

  70. Robertson G, Czerwinski M, Larson K, Robbins DC, Thiel D, van Dantzich M (1998) Data mountain: using spatial memory for document management. In: UIST'98: Proceedings of the 11th annual ACM symposium on user interface software and technology, San Fransisco, CA, ACM Press, New York, pp 153–162

    Chapter  Google Scholar 

  71. SAS (2006) Enterprise Miner. www.sas.com/technologies/analytics/datamining/miner/

  72. Sebag M, Schoenauer M (1988) Generation of rules with certainty and confidence factors from incomplete and incoherent learning bases. In: Proceedings of the European knowledge acquisition workshop EKAW'88, Gesellschaft für Mathematik und Datenverarbeitung mbH, pp 28.1–28.20

  73. Schneiderman B (2002) Inventing discovery tools: combining information visualization with data mining. Inform Visual 1(1):5–12

    Article  Google Scholar 

  74. Silberschatz A, Tuzhilin A (1996) User-assisted knowledge discovery: how much should the user be involved. Paper presented at the 1996 SIGMOD workshop on research issues on data mining and knowledge discovery (DMKD), Montreal, Canada

  75. Silberschatz A, Tuzhilin A (1996) What makes patterns interesting in knowledge discovery systems. IEEE Trans Knowl Data Eng 8(6):970–974

    Article  Google Scholar 

  76. Silverstein C, Brin S, Motwani R (1998) Beyond market baskets: generalizing association rules to dependence rules. Data Min Knowl Discov 2(1):39–68

    Article  Google Scholar 

  77. Simon HA (1979) Models of thought. Yale University Press, New Haven, CT

    Google Scholar 

  78. Spence I (1990) Visual psychophysics of simple graphical elements. J Exp Psychol Human Percept Perform 16(4):683–692

    Article  Google Scholar 

  79. Spence R (2000) Information visualization. Addison Wesley, Boston, MA

    Google Scholar 

  80. Srikant R, Vu Q, Agrawal R (1997) Mining association rules with item constraints. In: Proceedings of the 3rd ACM SIGKDD international conference on knowledge discovery and data mining, Washington DC. AAAI Press, Melno Park, pp 67–73

    Google Scholar 

  81. Suzuki E (2002) Undirected discovery of interesting exception rules. Int J Pattern Recog Artif Intell 16(8):1065–1086

    Article  Google Scholar 

  82. Tan P-N, Kumar V, Srivastava J (2004) Selecting the right objective measure for association analysis. Inform Syst 29(4):293–313

    Article  Google Scholar 

  83. Tufte E (1983) The visual display of quantitative information. Graphics Press, Cheshire, CT

    Google Scholar 

  84. Tuzhilin A, Adomavicius G (2002) Handling very large numbers of association rules in the analysis of microarray data. In: KDD'02: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining, Edmonton, Alberta, Canada ACM Press, New York, pp 396–404

    Chapter  Google Scholar 

  85. Unwin AR, Hofmann H, Bernt K (2001) The TwoKey plot for multiple association rules control. In: Proceedings of 5th European conference on principle and practice of knowledge discovery in databases (PKDD'01), Freiburg, Germany, Springer-Verlag, Berlin Heidelberg New York, pp 472–483

    Google Scholar 

  86. Ware C, Franck G (1996) Evaluating stereo and motion cues for visualizing information nets in three dimensions. ACM Trans Graphics 15(2):121–140

    Article  Google Scholar 

  87. Wilkinson L (1999) The Grammar Of Graphics. Springer-Verlag, Berlin Heidelberg New York

    MATH  Google Scholar 

  88. Wong PC, Whitney P, Thomas J (1999) Visualizing association rules for text mining. In: Proceedings of the 1999 IEEE symposium on information visualization, Berkeley, California, IEEE Computer Society, Washington, DC, pp 120–123

    Chapter  Google Scholar 

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Correspondence to Julien Blanchard.

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Julien Blanchard earned the Ph.D. in 2005 from Nantes University (France) and is currently an assistant professor at the Polytechnic School of Nantes University. He is the author of a book chapter and seven journal and international conference papers in the field of visualization and interestingness measures for data mining.

Fabrice Guillet is currently a member of the LINA laboratory (CNRS 2729) at the Polytechnic Graduate School of Nantes University (France). He receive the Ph.D. degree in computer science in 1995 from the Ecole Nationale Supěrieure des Télécommunications de Bretagne. He is author of 35 international publications in data mining and knowledge management. He is a founder and a permanent member of the Steering Committee of the annual EGC French-speaking conference.

Henri Briand received the Ph.D. degree in 1983 from Paul Sabatier University located in Toulouse (France) and has published works in over 100 publications in database systems and database mining. He was the head of the Computer Engineering Department at the Polytechnic School of Nantes University. He was in charge of a research team in the data mining domain. He is responsible for the organization of the Data Mining Master in Nantes University.

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Blanchard, J., Guillet, F. & Briand, H. Interactive visual exploration of association rules with rule-focusing methodology. Knowl Inf Syst 13, 43–75 (2007). https://doi.org/10.1007/s10115-006-0046-2

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