Subjective Interestingness in Association Rule Mining: A Theoretical Analysis

  • Rupal Sethi
  • B. ShekarEmail author
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 21)


The main aim of “Knowledge Discovery in Databases” is to extract and interpret interesting patterns present in real-world datasets. Measures to identify interesting patterns (called Interestingness Measures) may be categorized on the basis of statistical significance (Objective Measures), or on the basis of data and subjectivity of the user (Subjective Measures) which includes user’s domain knowledge. Three major steps in dealing with subjective measures are (1) knowledge acquisition from the user in terms of his beliefs, (2) the matching methodology for comparing generated association rules and user’s belief, and (3) generation of interesting rules that may be unexpected, novel or actionable. We propose and construct a theoretical framework for studying subjective interestingness in association rule mining, which takes care of these steps. We attempt to fit prior work done on subjective interestingness into this framework, thus identifying relevant research gaps. The notion of subjective interestingness confines to knowledge discovery by managers in a supermarket focusing on their expectations based on the data available. Perceptions behind customer purchases are not explicitly considered. We pose a major research question in subjective interestingness: What is the nature of subjective interestingness among associations of items, in terms of manager’s expectations and customers’ purchase patterns?


  1. 1.
    Adamo JM (2001) Data mining for association rules and sequential patterns: sequential and parallel algorithms. Springer, New YorkCrossRefGoogle Scholar
  2. 2.
    Aggarwal CC, Yu PS (1998) Mining large itemsets for association rules. IEEE Data Eng. Bull 21(1):23–31Google Scholar
  3. 3.
    Aggarwal CC (2015) Data mining: the textbook. SpringerCrossRefGoogle Scholar
  4. 4.
    Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. In Proceedings 20th international conference very large data bases, VLDB, 1215, pp 487–499Google Scholar
  5. 5.
    Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in large databases. ACM SIGMOD Rec 22(2):207–216CrossRefGoogle Scholar
  6. 6.
    Antonie ML, Zaïane OR (2004) Mining positive and negative association rules: an approach for confined rules. European conference on principles of data mining and knowledge discovery. Springer, Berlin, Heidelberg, pp 27–38Google Scholar
  7. 7.
    Barber MJ, Clark JW (2009) Detecting network communities by propagating labels under constraints. Phys Rev E 80(2):026129CrossRefGoogle Scholar
  8. 8.
    Basu S, Mooney RJ, Pasupuleti KV, Ghosh J (2001) Evaluating the novelty of text-mined rules using lexical knowledge. In: Proceedings of the seventh ACM SIGKDD international conference on knowledge discovery and data mining. ACM, pp 233–238Google Scholar
  9. 9.
    Becquet C, Blachon S, Jeudy B, Boulicaut JF, Gandrillon O (2002) Strong-association-rule mining for large-scale gene-expression data analysis: a case study on human SAGE data. Genome Biol 3(12), research0067-1CrossRefGoogle Scholar
  10. 10.
    Blanchard J, Guillet F, Gras R, Briand H (2005) Using information-theoretic measures to assess association rule interestingness. In: Fifth IEEE international conference on data mining, 8Google Scholar
  11. 11.
    Brin S, Motwani R, Silverstein C (1997) Beyond market baskets: generalizing association rules to correlations. AcmSigmod Rec 26(2):265–276 ACMCrossRefGoogle Scholar
  12. 12.
    Cai CH, Fu AWC, Cheng CH, Kwong WW (1998) Mining association rules with weighted items. In: Database engineering and applications symposium, 1998. Proceedings. IDEAS’98. International. IEEE, pp 68–77Google Scholar
  13. 13.
    Carvalho DR, Freitas AA, Ebecken N (2005) Evaluating the correlation between objective rule interestingness measures and real human interest. European conference on principles of data mining and knowledge discovery. Springer, Berlin, Heidelberg, pp 453–461Google Scholar
  14. 14.
    Chan R, Yang Q, Shen YD (2003) Mining high utility itemsets. In: Third IEEE international conference on data mining, 19–26Google Scholar
  15. 15.
    Chemero A (2003) An outline of a theory of affordances. Ecol Psychol 15(2):181–195CrossRefGoogle Scholar
  16. 16.
    Chen MS, Han J, Yu PS (1996) Data mining: an overview from a database perspective. IEEE Trans Knowl Data Eng 8(6):866–883CrossRefGoogle Scholar
  17. 17.
    Cios KJ, Pedrycz W, Swiniarski RW (1998) Data mining and knowledge discovery. Data mining methods for knowledge discovery. Springer, US, pp 1–26CrossRefGoogle Scholar
  18. 18.
    Duda R, Gaschnig J, Hart P (1979) Model design in the PROSPECTOR consultant system for mineral exploration. In: Expert systems in the microelectronic age, vol 1234, pp 153–167Google Scholar
  19. 19.
    Fayyad U, Piatetsky-Shapiro G, Smyth P (1996) From data mining to knowledge discovery in databases. AI Mag 17(3):37Google Scholar
  20. 20.
    Frawley WJ, Piatetsky-Shapiro G, Matheus CJ (1992) Knowledge discovery in databases: an overview. AI Mag 13(3):57Google Scholar
  21. 21.
    Freitas AA (1999) On rule interestingness measures. Knowl-Based Syst 12(5):309–315MathSciNetCrossRefGoogle Scholar
  22. 22.
    Galvao AB, Sato K (2005) Affordances in product architecture: linking technical functions and users’ tasks. In: ASME 2005 international design engineering technical conferences and computers and information in engineering conference. American Society of Mechanical Engineers, pp 143–153Google Scholar
  23. 23.
    Geng L, Hamilton HJ (2006) Interestingness measures for data mining: a survey. ACM Comput Surv (CSUR) 38(3):9CrossRefGoogle Scholar
  24. 24.
    Gibson JJ (1977) Perceiving, acting, and knowing: toward an ecological psychology. In: The theory of affordances, pp 67–82Google Scholar
  25. 25.
    Huynh XH, Guillet F, Briand H (2005) A data analysis approach for evaluating the behavior of interestingness measures. Discovery science. Springer, Berlin, Heidelberg, pp 330–337CrossRefGoogle Scholar
  26. 26.
    Kamber M, Shinghal R (1996) Evaluating the Interestingness of characteristic rules. In: KDD, pp 263–266Google Scholar
  27. 27.
    Kannan S, Bhaskaran R (2009) Association rule pruning based on interestingness measures with clustering. arXiv:0912.1822
  28. 28.
    Kontonasios KN, Spyropoulou E, De Bie T (2012) Knowledge discovery interestingness measures based on unexpectedness. Wiley Interdiscip Rev: Data Min Knowl Discov 2(5):386–399Google Scholar
  29. 29.
    Lallich S, Teytaud O, Prudhomme E (2007) Association rule interestingness: measure and statistical validation. In: Quality measures in data mining. Springer, Berlin, Heidelberg, pp 251–275CrossRefGoogle Scholar
  30. 30.
    Lavrač N, Flach P, Zupan B (1999) Rule evaluation measures: a unifying view. Springer, Berlin, Heidelberg, pp 174–185CrossRefGoogle Scholar
  31. 31.
    Lenca P, Meyer P, Vaillant B, Lallich S (2008) On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid. Eur J Oper Res 184(2):610–626CrossRefGoogle Scholar
  32. 32.
    Lenca P, Vaillant B, Meyer P, Lallich S (2007) Association rule interestingness measures: experimental and theoretical studies. Quality measures in data mining. Springer, Berlin, Heidelberg, pp 51–76CrossRefGoogle Scholar
  33. 33.
    Leonardi PM (2013) When does technology use enable network change in organizations? A comparative study of feature use and shared affordances. Manag Inf Syst Q 37(3):749–775CrossRefGoogle Scholar
  34. 34.
    Liao SH, Chen YJ, Lin YT (2011) Mining customer knowledge to implement online shopping and home delivery for hypermarkets. Expert Syst Appl 38(4):3982–3991CrossRefGoogle Scholar
  35. 35.
    Ling CX, Chen T, Yang Q, Cheng J (2002) Mining optimal actions for profitable CRM. In: IEEE international conference on data mining, pp 767–770Google Scholar
  36. 36.
    Liu B, Hsu W (1996) Post-analysis of learned rules. AAAI/IAAI 1:828–834Google Scholar
  37. 37.
    Liu B, Hsu W, Chen S (1997) Using general impressions to analyze discovered classification rules. In: KDD, pp 31–36Google Scholar
  38. 38.
    Liu B, Hsu W, Ma Y (1999) Mining association rules with multiple minimum supports. In: Proceedings of the fifth ACM SIGKDD international conference on knowledge discovery and data mining, pp 337–341Google Scholar
  39. 39.
    Liu DR, Shih YY (2005) Integrating AHP and data mining for product recommendation based on customer lifetime value. Info Manag, 42(3):387–400CrossRefGoogle Scholar
  40. 40.
    Lu S, Hu H, Li F (2001) Mining weighted association rules. Intell Data Anal 5(3):211–225zbMATHGoogle Scholar
  41. 41.
    Major JA, Mangano JJ (1995) Selecting among rules induced from a hurricane database. J Intell Inf Syst 4(1):39–52CrossRefGoogle Scholar
  42. 42.
    McGarry K (2005) A survey of interestingness measures for knowledge discovery. Knowl Eng Rev 20(01):39–61CrossRefGoogle Scholar
  43. 43.
    Ng RT, Lakshmanan LV, Han J, Pang A (1998) Exploratory mining and pruning optimizations of constrained associations rules. ACM SIGMOD Rec 27(2):13–24CrossRefGoogle Scholar
  44. 44.
    Norman DA (2013) The design of everyday things: revised and expanded edition. Basic BooksGoogle Scholar
  45. 45.
    Ohsaki M, Kitaguchi S, Yokoi H, Yamaguchi T (2005) Investigation of rule interestingness in medical data mining. Active mining. Springer, Berlin, Heidelberg, pp 174–189CrossRefGoogle Scholar
  46. 46.
    Padmanabhan B, Tuzhilin A (1999) Unexpectedness as a measure of interestingness in knowledge discovery. Decis Support Syst 27(3):303–318CrossRefGoogle Scholar
  47. 47.
    Pei J, Han J, Lakshmanan LV (2004) Pushing convertible constraints in frequent itemset mining. Data Min Knowl Disc 8(3):227–252MathSciNetCrossRefGoogle Scholar
  48. 48.
    Piatetsky-Shapiro G, Matheus CJ (1994) The interestingness of deviations. In: Proceedings of AAAI workshop on knowledge discovery in databasesGoogle Scholar
  49. 49.
    Raghavan S, Mooney RJ (2013) Online inference-rule learning from natural-language extractions. In: AAAI workshop: statistical relational artificial intelligenceGoogle Scholar
  50. 50.
    Roddick JF, Spiliopoulou M (2002) A survey of temporal knowledge discovery paradigms and methods. IEEE Trans Knowl Data Eng 14(4):750–767CrossRefGoogle Scholar
  51. 51.
    Savasere A, Omiecinski E, Navathe S (1998) Mining for strong negative associations in a large database of customer transactions. In: 14th IEEE international conference on data engineering, pp 494–502Google Scholar
  52. 52.
    Silberschatz A, Tuzhilin A (1996) What makes patterns interesting in knowledge discovery systems. IEEE Trans Knowl Data Eng 8(6):970–974CrossRefGoogle Scholar
  53. 53.
    Srikant R, Vu Q, Agrawal R (1997) Mining association rules with item constraints. KDD 97:67–73Google Scholar
  54. 54.
    Stoffregen TA (2003) Affordances as properties of the animal-environment system. Ecol Psychol 15(2):115–134CrossRefGoogle Scholar
  55. 55.
    Swesi IMAO, Bakar AA, Kadir ASA (2012) Mining positive and negative association rules from interesting frequent and infrequent itemsets. In: 9th IEEE international conference on fuzzy systems and knowledge discovery (FSKD), pp 650–655Google Scholar
  56. 56.
    Tan PN, Kumar V, Srivastava J (2002) Selecting the right interestingness measure for association patterns. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 32–41Google Scholar
  57. 57.
    Tew C, Giraud-Carrier C, Tanner K, Burton S (2014) Behavior-based clustering and analysis of interestingness measures for association rule mining. Data Min Knowl Disc 28(4):1004–1045MathSciNetCrossRefGoogle Scholar
  58. 58.
    Turvey MT (1992) Affordances and prospective control: an outline of the ontology. Ecol Psychol 4(3):173–187CrossRefGoogle Scholar
  59. 59.
    Tuzhilin A, Adomavicius G (2002) Handling very large numbers of association rules in the analysis of microarray data. In: Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, pp 396–404Google Scholar
  60. 60.
    Vaillant B, Lenca P, Lallich S (2004) A clustering of interestingness measures. Discovery science. Springer, Berlin, Heidelberg, pp 290–297CrossRefGoogle Scholar
  61. 61.
    Wang H (1997) Intelligent agent-assisted decision support systems: integration of knowledge discovery, knowledge analysis, and group decision support. Expert Syst Appl 12(3):323–335CrossRefGoogle Scholar
  62. 62.
    Wang K, Tang L, Han J, Liu J (2002) Top down fp-growth for association rule mining. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, Heidelberg, pp 334–340CrossRefGoogle Scholar
  63. 63.
    Warren WH (1984) Perceiving affordances: visual guidance of stair climbing. J Exp Psychol Hum Percept Perform 10(5):683CrossRefGoogle Scholar
  64. 64.
    Wei JM, Yi WG, Wang MY (2006) Novel measurement for mining effective association rules. Knowl-Based Syst 19(8):739–743CrossRefGoogle Scholar
  65. 65.
    Wu ST, Li Y, Xu Y, Pham B, Chen P (2004) Automatic pattern-taxonomy extraction for web mining. In: Proceedings of the 2004 IEEE/WIC/ACM international conference on web intelligence. IEEE Computer Society, pp 242–248Google Scholar
  66. 66.
    Yao H, Hamilton HJ (2006) Mining itemset utilities from transaction databases. Data Knowl Eng 59(3):603–626CrossRefGoogle Scholar
  67. 67.
    Yao YY, Zhong N (1999) An analysis of quantitative measures associated with rules. Methodologies for knowledge discovery and data mining. Springer, Berlin, Heidelberg, pp 479–488CrossRefGoogle Scholar
  68. 68.
    Yuan X, Buckles BP, Yuan Z, Zhang J (2002) Mining negative association rules. In: Proceedings of Seventh International Symposium on Computers and Communications, pp 623–628Google Scholar
  69. 69.
    Yun H, Ha D, Hwang B, Ryu KH (2003) Mining association rules on significant rare data using relative support. J Sys Soft, 67(3):181–191CrossRefGoogle Scholar
  70. 70.
    Zhang C, Zhang S (2002) Association rule mining: models and algorithms. SpringerGoogle Scholar
  71. 71.
    Zhang H, Padmanabhan B, Tuzhilin A (2004) On the discovery of significant statistical quantitative rules. In: Proceedings of the tenth ACM SIGKDD international conference on knowledge discovery and data mining, pp 374–383Google Scholar
  72. 72.
    Zhong N, Yao YY, Ohishima M (2003) Peculiarity oriented multidatabase mining. IEEE Trans Knowl Data Eng 15(4):952–960CrossRefGoogle Scholar

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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Decision Sciences and Information Systems AreaIndian Institute of Management BangaloreBangaloreIndia

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