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Interestingness Measures - On Determining What Is Interesting

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Data Mining and Knowledge Discovery Handbook

Summary

As the size of databases increases, the sheer number of mined from them can easily overwhelm users of the KDD process. Users run the KDD process because they are overloaded by data. To be successful, the KDD process needs to extract interesting patterns from large masses of data. In this chapter we examine methods of tackling this challenge: how to identify interesting patterns.

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References

  • Adomavicius, G. and Tuzhilin, A. (1997). Discovery of actionable patterns in databases: The action hierarchy approach. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pages 111–114, Newport Beach, CA, USA. AAAI Press.

    Google Scholar 

  • Adomavicius, G. and Tuzhilin, A. (2001). Expert-driven validation of rule-based user models in personalization applications. Data Mining and Knowledge Discovery, 5(1/2):33–58.

    Article  MATH  Google Scholar 

  • Aggarwal, C. C. and Yu, P. S. (1998). A new approach to online generation of association rules. Technical Report Research Report RC 20899, IBM T J Watson Research Center.

    Google Scholar 

  • Agrawal, R., Heikki, M., Srikant, R., Toivonen, H., and Verkamo, A. I. (1996). Advances in Knowledge Discovery and Data Mining, chapter 12: Fast Discovery of Association Rules, pages 307–328. AAAI Press/The MIT Press, Menlo Park, California.

    Google Scholar 

  • Basu, S., Mooney, R. J., Pasupuleti, K. V., and 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, pages 233–238, San Francisco, CA, USA.

    Google Scholar 

  • Bayardo Jr., R. J. and Agrawal, R. (1999). Mining the most interesting rules. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 145–154, San Diego, CA.

    Google Scholar 

  • Bayardo Jr., R. J., Agrawal, R., and Gunopulos, D. (1999). Constraint-based rule mining in large, dense databases. In Proceedings of the Fifteenth IEEE ICDE International Conference on Data Engineering, pages 188–197, Sydney, Australia.

    Google Scholar 

  • Brin, S., Motwani, R., and Silverstein, C. (1997). Beyond market baskets: Generalizing association rules to correlations. In Proceedings of ACM SIGMOD International Conference on Management of Data, pages 265–276, Tucson, AZ, USA.

    Google Scholar 

  • Fayyad, U. M., Piatetsky-Shapiro, G., and Smyth, P. (1996). Advances in Knowledge Discovery and Data Mining, chapter 1: From Data Mining to Knowledge Discovery: An Overview, pages 1–34. AAAI Press.

  • Hilderman, R. J. and Hamilton, H. J. (2000). Principles for mining summaries using objective measures of interestingness. In Proceedings of the Twelfth IEEE International Conference on Tools with Artificial Intelligence (ICTAI), pages 72–81, Vancouver, Canada.

    Google Scholar 

  • Hilderman, R. J. and Hamilton, H. J. (2001). Knowledge Discovery and Measures of Interest. Kluwer Academic Publishers.

    Google Scholar 

  • Hipp, J. and Günter, U. (2002). Is pushing constraints deeply into the mining algorithms really what we want? SIGKDD Explorations, 4(1): 50–55.

    Article  Google Scholar 

  • Kamber, M. and Shinghal, R. (1996). Evaluating the interestingness of characteristic rules. In Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pages 263–266, Portland, OR, USA.

    Google Scholar 

  • Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., and Verkam, A. I. (1994). Finding interesting rules from large sets of discovered association rules. In Proceedings of the Third ACM CIKM International Conference on Information and Knowledge Management, pages 401–407, Orlando, FL, USA. ACM Press.

    Google Scholar 

  • Klösgen, W. (1996). Advances in Knowledge Discovery and Data Mining, chapter 10: Explora: a Multipattern and Multistrategy Discovery Assistant, pages 249–271. AAAI Press.

  • Liu, B., Hsu, W., and Chen, S. (1997). Using general impressions to analyze discovered classification rules. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pages 31–36, Newport Beach, CA, USA. AAAI Press.

    Google Scholar 

  • Liu, B., Hsu, W., and Ma, Y. (1999). Pruning and summarizing the discovered associations. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 125–134, San Diego, CA, USA.

    Google Scholar 

  • Liu, B., Hsu, W., and Ma, Y. (2001a). Discovery the set of fundamental rule changes. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 335–340, San Francisco, CA, USA.

    Google Scholar 

  • Liu, B., Hsu, W., and Ma, Y. (2001b). Identifying non-actionable association rules. In Proceedings of the Seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 329–334, San Francisco, CA, USA.

    Google Scholar 

  • Liu, B., Hu, M., and Hsu, W. (2000). Multi-level organization and summarization of the discovered rules. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 208–217, Boston, MA, USA.

    Google Scholar 

  • Major, J. A. and Mangano, J. J. (1995). Selecting among rules induced from a hurricane databases. Journal of Intelligent Information Systems, 4:39–52.

    Article  Google Scholar 

  • Ng, R. T., Lakshmanan, L. V. S., Han, J., and Pang, A. (1998). Exploratory mining and pruning optimizations of constrained association rules. In Proceedings of ACM SIGMOD International Conference on Management of Data, pages 13–24.

    Google Scholar 

  • Padmanabhan, B. and Tuzhilin, A. (2000). Small is beautiful: Discovering the minimal set of unexpected patterns. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 54–63, Boston, MA, USA.

    Google Scholar 

  • Piatetsky-Shapiro, G. (1991). Knowledge Discovery in Databases, chapter 13: Discovery, Analysis, and Presentation of Strong Rules, pages 248–292. AAAI/MIT Press.

  • Rokach, L., Averbuch, M., and Maimon, O., Information retrieval system for medical narrative reports. Lecture notes in artificial intelligence, 3055. pp. 217-228, Springer-Verlag (2004).

    Google Scholar 

  • Sahar, S. (1999). Interestingness via what is not interesting. In Proceedings of the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 332–336, San Diego, CA, USA.

    Google Scholar 

  • Sahar, S. (2001). Interestingness preprocessing. In Proceedings of the IEEE ICDM International Conference on Data Mining, pages 489–496, San Jose, CA, USA.

    Google Scholar 

  • Sahar, S. (2002a). Exploring interestingness through clustering: A framework. In Proceedings of the IEEE ICDM International Conference on Data Mining, pages 677–680, Maebashi City, Japan.

    Google Scholar 

  • Sahar, S. (2002b). On incorporating subjective interestingness into the mining process. In Proceedings of the IEEE ICDM International Conference on Data Mining, pages 681–684, Maebashi City, Japan.

    Google Scholar 

  • Sahar, S. and Mansour, Y. (1999). An empirical evaluation of objective interestingness criteria. In SPIE Conference on Data Mining and Knowledge Discovery, pages 63–74, Orlando, FL, USA.

    Google Scholar 

  • Shah, D., Lakshmanan, L. V. S., Ramamritham, K., and Sudarshan, S. (1999). Interestingness and pruning of mined patterns. In Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD), Philadelphia, PA, USA.

    Google Scholar 

  • Silberschatz, A. and Tuzhilin, A. (1996). What makes patterns interesting in knowledge discovery systems. IEEE Transactions on Knowledge and Data Engineering (TKDE), 8(6):970–974.

    Article  Google Scholar 

  • Spiliopoulou, M. and Roddick, J. F. (2000). Higher order mining: Modeling and mining the results of knowledge discovery. In Proceedings of the Second Conference on Data Mining Methods and Databases, pages 309–320, Cambridge, UK. WIT Press.

    Google Scholar 

  • Srikant, R., Vu, Q., and Agrawal, R. (1997). Mining association rules with item constraints. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining, pages 67–73, Newport Beach, CA, USA. AAAI Press.

    Google Scholar 

  • Subramonian, R. (1998). Defining diff as a Data Mining primitive. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, pages 334–338, New York City, NY, USA. AAAI Press.

    Google Scholar 

  • Tan, P.-N., Kumar, V., and Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. In Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 32–41, Edmonton, Alberta, Canada.

    Google Scholar 

  • Toivonen, H., Klemettinen, M., Ronkainen, P., Hätönen, K., and Mannila, H. (1995). Pruning and grouping discovered association rules. In Proceedings of the MLnet Familiarization Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases, pages 47–52, Heraklion, Crete, Greece.

    Google Scholar 

  • Tuzhilin, A. and Adomavicius, G. (2002). Handling very large numbers of association rules in the analysis of microarray data. In Proceedings of the Eight ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 396–404, Edmonton, Alberta, Canada.

    Google Scholar 

  • Zaki, M. J. (2000). Generating non-redundant association rules. In Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 34–43, Boston, MA, USA.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Tel-Aviv University, Tel-Aviv, Israel

    Sigal Sahar

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  1. Sigal Sahar
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Correspondence to Sigal Sahar .

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Editors and Affiliations

  1. , Dept. Industrial Engineering, Tel Aviv University, Ramat Aviv, 69978, Israel

    Oded Maimon

  2. , Dept. Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, 84105, Israel

    Lior Rokach

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Sahar, S. (2009). Interestingness Measures - On Determining What Is Interesting. In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09823-4_30

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  • DOI: https://doi.org/10.1007/978-0-387-09823-4_30

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  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-0-387-09822-7

  • Online ISBN: 978-0-387-09823-4

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