Interestingness Measures: A Selected Bibliography

AGGARWAL, C.C. AND YU, P.S., A new framework for itemset generation, Proceedings of the seventeenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems, p.18-24, June 01-04, 1998, Seattle, Washington, United States
AGRAWAL, R. AND SRIKANT, R. 1994. Fast algorithms for mining association rules. In Proceedings of the 20th International Conference on Very Large Databases. Santiago, Chile. 487–499.
AZE, J., KONDRATOFF, Y., 2002. Evaluation de la re´sistance au bruit de quelques mesures d’extraction de re`gles d’assocation. Extraction des connaissances et apprentissage (EGC 2002) 1
BRIN, S., MOTWANI, R., ULLMAN, J., AND TSUR, S. 1997. Dynamic Itemset Counting and Implication Rules for Market Basket Data. In Ptoc. of the 1997 ACMSIGMOD Int’l Conf on the Management of Data, 255-264.
BARBER, B. AND HAMILTON, H. J. 2003. Extracting share frequent itemsets with infrequent subsets. Data Mining Knowl. Discovery 7, 2, 153–185.
BASTIDE, Y., PASQUIER, N., TAOUIL, R., STUMME, G., AND LAKHAL, L. 2000. Mining minimal nonredundant association rules using frequent closed itemsets. In Proceedings of the Ist International Conference on Computational Logic. London, UK. 972–986.
BAY, S. D. AND PAZZANI, M. J. 1999. Detecting change in categorical data: Mining contrast sets. In Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining (KDD-99). San Diego, CA. 302–306.
BAYARDO, R. J. AND AGRAWAL R. 1999. Mining the most interesting rules. In Proceedings of the 5th International Conference on Knowledge Discovery and Data Mining (KDD-99). San Diego, CA. 145–154.
BREIMAN, L., FREIDMAN, J., OLSHEN, R., AND STONE, C. 1984. Classification and Regression Trees. Wadsworth and Brooks, Pacific Grove, CA.
CAI, C. H., FU, A. W., CHENG, C. H., AND KWONG, W. W. 1998. Mining association rules with weighted items. In Proceedings of the International Database Engineering and Applications Symposium (IDEAS ’98). Cardiff, UK. 68–77.
CARTER, C. L., HAMILTON, H. J., AND CERCONE, N. 1997. Share-Based measures for itemsets. In Proceedings of the Ist European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD ’97). Trondheim, Norway. 14–24.
CARVALHO, D. R. AND FREITAS, A. A. 2000. A genetic algorithm-based solution for the problem of small disjuncts. In Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2000). Lyon, France. 345–352.
CHAN, R., YANG, Q., AND SHEN, Y. 2003. Mining high-utility itemsets. In Proceedings of the 3rd IEEE International Conference on Data Mining (ICDM ’03). Melbourne, FL. 19–26.
CHURCH, K.W., HANKS, P., 1990. Word association norms, mutual information an lexicography. Computational Linguistics 16 (1), 22–29.
CLARK, P. AND BOSWELL, R. 1991. Rule induction with CN2: Some recent improvements. In Proceedings of the 5th European Working Session on Learning (EWSL ’91). Porto, Portugal. 151–163.
DONG, G. AND LI, J. 1998. Interestingness of discovered association rules in terms of neighborhood-based unexpectedness. In Proceedings of the 2nd Pacific Asia Conference on Knowledge Discovery in Databases(PAKDD-98). Melbourne, Australia. 72–86.
FABRIS, C. C. AND FREITAS, A. A. 2001. Incorporating deviation-detection functionality into the OLAP paradigm. In Proceedings of the 16th Brazilian Symposium on Databases (SBBD 2001). Rio de Janeiro,Brazil. 274–285.
FAYYAD, U. M., PIATETSKY-SHAPIRO, G., AND SMYTH, P. 1996. From data mining to knowledge discovery: An overview. In Advances in Knowledge Discovery and Data Mining, U. M. Fayyad et al., Eds. MIT Press. Cambridge, MA, 1–34.
FORSYTH, R. S., CLARKE, D. D., AND WRIGHT, R. L. 1994. Overfitting revisited: An information-theoretic approach to simplifying discrimination trees. J. Exp. Theor. Artif. Intell. 6, 289–302.
FREITAS, A. A. 1998. On objective measures of rule surprisingness. In Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD ’98). Nantes, France. 1–9.
FURNKRANZ, J. AND FLACH, P. A. 2005. ROC ‘n’ rule learning: Towards a better understanding of covering algorithms. Mach. Learn. 58, 1, 39–77.
GRAS, R., KUNTZ, P., COUTURIER, R., AND GUILLET, F., Une version entropique de l’intensit´e d’implication pour les corpus volumineux. Extraction des connaissances et apprentissage, volume 1, pages 69– 80. Hermes, 2001.
GRAY, B. AND ORLOWSKA, M. E. 1998. CCAIIA: Clustering categorical attributes into interesting association rules. In Proceedings of the 2nd Pacific Asia Conference on Knowledge Discovery and Data Mining(PAKDD-98). Melbourne, Australia. 132–143.
HAMILTON, H. J., GENG, L., FINDLATER, L., AND RANDALL, D. J. 2006. Efficient spatio-temporal data mining with GenSpace graphs. J. Appl. Logic 4, 2, 192–214.
HILDERMAN, R. J., CARTER, C. L., HAMILTON, H. J., AND CERCONE, N. 1998. Mining market basket data using share measures and characterized itemsets. In Proceedings of the 2nd Pacific Asia Conference on Knowledge Discovery in Databases (PAKDD-98). Melbourne, Australia. 72–86.
HILDERMAN, R. J. AND HAMILTON, H. J. 2001. Knowledge Discovery and Measures of Interest. Kluwer Academic, Boston, MA.
HOAGLIN, D. C., MOSTELLER, F., AND TUKEY, J. W., EDS. 1985. Exploring Data Tables, Trends, and Shapes. Wiley, New York.
JAROSZEWICZ, S. AND SIMOVICI, D. A. 2001. A general measure of rule interestingness. In Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery (PKDD 2001). Freiburg,Germany. 253–265.
KLOSGEN, W. 1996. Explora: A multipattern and multistrategy discovery assistant. In Advances in Knowledge Discovery and Data Mining, U. M. Fayyad et al., Eds. MIT Press, Cambridge, MA, 249–271.
MORIMOTO, Y., FUKUDA, T., MATSUZAWA, H., TOKUYAMA, T., AND YODA, K. 1998. Algorithms for Mining Association Rules for Binary Segmentations of Huge Categorical Databases. In Ptoc. of the 24th Very Large Data Bases Conf., 380-39 1.
PIATETSKY-SHAPIRO, G., "Discovery, Analysis, and Presentation of Strong Rules" (in Knowledge Discovery in Databases 1991), pp. 229-248.
TAN, P., KUMAR, V., and SRIVASTAVA, J. Selecting the right interestingness measure for association patterns. Proc. of the 8th Int. Conf. on Knowledge Discovery and Data Mining, 2002, p. 32-41.
VAILLANT, B., LALLICH, S. AND LENCA, P. Modeling of the counter-examples and association rules interestingness measures behavior
ZHANG, T., 2000. Association rules. In Knowledge Discovery and Data Mining, Current Issues and New Applications, 4th Pacific-Asia Conference, PADKK 2000, Kyoto, Japan, Proceedings, Lecture Notes in Computer Science, vol. 1805. Springer.