A Tree-Based Contrast Set-Mining Approach to Detecting Group Differences

Published Online:https://doi.org/10.1287/ijoc.2013.0558

References

  • Agrawal R, Srikant R (1994) Fast algorithms for mining association rules. Proc. 20th VLDB Conf. Santiago, Chile, 487–499.Google Scholar
  • Alqadah F, Bhatnagar R (2009) Discovering substantial distinctions among incremental bi-clusters. Proc. 9th SIAM Internat. Conf. Data Mining, Nevada, 197–208.CrossrefGoogle Scholar
  • Bay SD, Pazzani MJ (1999) Detecting change in categorical data: Mining contrast sets. ACM SIGKDD Internat. Conf. Knowledge Discovery and Data Mining, San Diego, 302–306.CrossrefGoogle Scholar
  • Bay SD, Pazzani MJ (2001) Detecting group differences: Mining contrast sets. Data Mining Knowledge Discovery 5(3):213–246.CrossrefGoogle Scholar
  • Breiman L (2001) Random Forests. Machine Learn. 45(1):5–32.CrossrefGoogle Scholar
  • Breiman L, Friedman J, Stone CJ, Olshen RA (1984) Classification and Regression Trees (Chapman and Hall, New York).Google Scholar
  • Darity WA (2000) Racial and ethnic economic inequality: The international record. Amer. Econom. Rev. 90(2):308–311.CrossrefGoogle Scholar
  • Deng K, Zaïane OR (2009) Contrasting sequence groups by emerging sequences. Lecture Notes in Computer Science, Vol. 5808/2009 (Springer, Berlin), 377–384.CrossrefGoogle Scholar
  • Dong G, Li J (1999) Efficient mining of emerging patterns: Discovering trends and differences. Proc. Fifth ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York),43–52.CrossrefGoogle Scholar
  • Fan H, Ramamohanara K (2003) A Bayesian approach to use emerging patterns for classification. Proc. 14th Australasian Database Conf. (ADC-03), Adelaide, Australia, 39–48.Google Scholar
  • Fan H, Fan M, Ramamohanarao K, Liu M (2006) Further improving emerging pattern based classifiers via bagging. Proc. 10th Pacific-Asia Conf. Knowledge Discovery Data Mining (PAKDD-06), Singapore, 91–96.CrossrefGoogle Scholar
  • Frank A, Asuncion A (2010) UCI Machine Learning Repository. School of Information and Computer Science, University of California, Irvine. Accessed August 2011, http://archive.ics.uci.edu/ml.Google Scholar
  • Han J, Pei J, Yin Y, Mao R (2004) Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining Knowledge Discovery 8(1):53–87.CrossrefGoogle Scholar
  • Hilderman RJ, Peckham T (2007) Statistical methodologies for mining potentially interesting contrast sets. Stud. Comput. Intelligence 43:153–177.CrossrefGoogle Scholar
  • Kobyliński L, Walczak K (2011) Efficient mining of jumping emerging patterns with occurrence counts for classification. Transactions on Rough Sets XIII. Lecture Notes in Computer Science, Vol. 6499/2011 (Springer, Berlin), 73–88.CrossrefGoogle Scholar
  • Kralj P, Lavrac N, Gamberger D, Krstačić A (2007a) Contrast set mining for distinguishing between similar diseases. Proc. 11th Conf. Artificial Intelligence in Medicine (AIME-07) (Springer, Berlin), 109–118.CrossrefGoogle Scholar
  • Kralj P, Lavrac N, Gamberger D, Krstacic A (2007b) Contrast set mining through subgroup discovery applied to brain ischaemia data. Proc. 11th Pacific-Asia Conf. Adv. Knowledge Discovery and Data Mining: (PAKDD-07) (Springer, Berlin), 579–586.CrossrefGoogle Scholar
  • Lavrac N, Kavsek B, Flach PA, Todorovski L (2004) Subgroup discovery with CN2-SD. J. Machine Learn. Res. 5:153–188.Google Scholar
  • Liu B, Hsu W, Ma Y (2001) Discovering the set of fundamental rule changes. Proc. 7th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (KDD-01) (ACM, New York), 335–340.CrossrefGoogle Scholar
  • Liu B, Hsu W, Han H-S, Xia Y (2000) Mining changes for real-life applications. Proc. 2nd Internat. Conf. Data Warehousing Knowledge Discovery (DaWaK-2000) (Springer, Berlin), 337–346.CrossrefGoogle Scholar
  • Loekito E, Bailey J (2006) Fast mining of high dimensional expressive contrast patterns using zero-suppressed binary decision diagrams. Proc. 12th ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 307–316.CrossrefGoogle Scholar
  • Loekito E, Bailey J (2008) Mining influential attributes that capture class and group contrast behavior. Proc. 17th ACM Conf. Inform. Knowledge Management, Napa Valley, CA, 971–980.CrossrefGoogle Scholar
  • Minaei-Bidgoli B, Tan PN, Punch WF (2004) Mining interesting contrast rules for a web-based educational system. Internat. Conf. Machine Learn. Appl. (IEEE Computer Society, Louisville, KY), 1–8.CrossrefGoogle Scholar
  • Nazeri Z, Barbara D, De Jong K, Donohue G, Sherry L (2008) Contrast-Set Mining of Aircraft Accidents and Incidents (Springer-Verlag, Berlin, Heidelberg).CrossrefGoogle Scholar
  • Quinlan JR (1993) C4.5: Programs for Machine Learning (Morgan Kaufman Publishers, San Francisco).Google Scholar
  • Ramamohanarao K (2010) Contrast pattern mining and its application for building robust classifiers. Pfahringer B, Holmes G, Hoffmann A, eds. Discovery Science. Lecture Notes in Computer Science, Vol. 6332/2010 (Springer, Berlin), 380.CrossrefGoogle Scholar
  • Ramamohanarao K, Bailey J, Fan H (2005) Efficient mining of contrast patterns and their applications to classification. Proc. Third Internat. Conf. Intelligent Sensing Inform. Processing (IEEE Computer Society, Washington, DC), 39–47.CrossrefGoogle Scholar
  • Ruggles S (1997) The rise of divorce and separation in the United States, 1880–1990. Demography 34(4):455–466.CrossrefGoogle Scholar
  • Satsangi A, Zaiane OR (2007) Contrasting the contrast sets: An alternative approach. 11th Internat. Database Engrg. Appl. Sympos., Alberta, Canada, 114–119.CrossrefGoogle Scholar
  • Siu KKW, Butler SM, Beveridge T, Gillam JE, Hall CJ, Kaye AH, Lewis RAet al. (2005) Identifying markers of pathology in SAXS data of malignant tissues of the brain. Nuclear Instruments Methods Phys. Res. Sect. A 548(1–2):140–146.CrossrefGoogle Scholar
  • Song HS, Kim JK, Kim SH (2001) Mining the change of customer behavior in an internet shopping mall. Expert Systems Appl. 21(3):157–168.CrossrefGoogle Scholar
  • Wang K, Zhou S, Fu AW-C, Yu JX (2003) Mining changes of classification by correspondence tracing. Proc. 3rd SIAM Internat. Conf. Data Mining (SDM-03), San Francisco, 95–106.CrossrefGoogle Scholar
  • Webb GI (2000) Efficient search for association rules. Sixth ACM SIGKDD Int. Conf. Knowledge Discovery Data Mining, New York, 99–107.CrossrefGoogle Scholar
  • Webb GI (2001) Magnum Opus version 1.3. Computer software, Distributed by Rulequest Research, http://www.rulequest.com.Google Scholar
  • Webb GI, Butler SM, Newlands D (2003) On detecting differences between groups. Proc. ACM SIGKDD Internat. Conf. Knowledge Discovery Data Mining (ACM, New York), 256–265.CrossrefGoogle Scholar
  • Wong T, Tseng K-L (2005) Mining negative contrast sets from data with discrete attributes. Expert Systems Appl. 29(2):401–407.CrossrefGoogle Scholar
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