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Journal of Intelligent Information Systems

Erschienen in:

01.12.2015

Objectively evaluating condensed representations and interestingness measures for frequent itemset mining

verfasst von: Albrecht Zimmermann

Erschienen in: Journal of Intelligent Information Systems | Ausgabe 3/2015

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Abstract

Itemset mining approaches, while having been studied for more than 15 years, have been evaluated only on a handful of data sets. In particular, they have never been evaluated on data sets for which the ground truth was known. Thus, it is currently unknown whether itemset mining techniques actually recover underlying patterns. Since the weakness of the algorithmically attractive support/confidence framework became apparent early on, a number of interestingness measures have been proposed. Their utility, however, has not been evaluated, except for attempts to establish congruence with expert opinions. Using an extension of the Quest generator proposed in the original itemset mining paper, we propose to evaluate these measures objectively for the first time, showing how many non-relevant patterns slip through the cracks.

Vorheriger Artikel Guest editor’s introduction: special issue on quality issues, measures of interestingness and evaluation of data mining models

Nächster Artikel Subspace clustering of data streams: new algorithms and effective evaluation measures

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Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In 20th VLDB (pp. 487–499). Chile: Morgan Kaufmann.

Bayardo, R.J. Jr., Goethals, B., Zaki, M.J. (Eds.) (2004). FIMI 04, proceedings of the IEEE ICDM workshop on FIM implementations. Brighton.

Bie, T.D. (2011). Maximum entropy models and subjective interestingness: an application to tiles in binary databases. Data Mining and Knowledge Discovery, 23(3), 407–446.MATHMathSciNetCrossRef

Blake, C., & Merz, C. (1998). UCI repository of machine learning databases. http://www.ics.uci.edu/mlearn/MLRepository.html.

Blanchard, J., Guillet, F., Gras, R., Briand, H. (2005). Using information-theoretic measures to assess association rule interestingness. In J. Han, B.W. Wah, V. Raghavan, X. Wu, R. Rastogi (Eds.), ICDM (pp. 66–73). Houston: IEEE.

Boulicaut, J.F., & Jeudy, B. (2001). Mining free itemsets under constraints. In M.E. Adiba, C. Collet, B.C. Desai (Eds.), IDEAS ’01 (pp. 322–329).

Brin, S., Motwani, R., Silverstein, C. (1997). Beyond market baskets: generalizing association rules to correlations. In J. Peckham (Ed.), (pp. 265–276).

Carvalho, D., Freitas, A., Ebecken, N. (2005). Evaluating the correlation between objective rule interestingness measures and real human interest. In A. Jorge, L. Torgo, P. Brazdil, R. Camacho, J. Gama (Eds.), PKDD (pp. 453–461). Springer.

Cooper, C., & Zito, M. (2007). Realistic synthetic data for testing association rule mining algorithms for market basket databases. In J.N. Kok, J. Koronacki, R.L. de Mántaras, S. Matwin, D. Mladenic, A. Skowron (Eds.), PKDD (pp. 398–405). Springer.

Gouda, K., & Zaki, M.J. (2005). Genmax: an efficient algorithm for mining maximal frequent itemsets. Data Mining and Knowledge Discovery, 11(3), 223–242.MathSciNetCrossRef

Han, J., Pei, J., Yin, Y. (2000). Mining frequent patterns without candidate generation. In W. Chen, J.F. Naughton, P.A. Bernstein (Eds.), SIGMOD conference (pp. 1–12). ACM.

Heikinheimo, H., Seppänen, J.K., Hinkkanen, E., Mannila, H., Mielikäinen, T. (2007). Finding low-entropy sets and trees from binary data. In P. Berkhin, R. Caruana, X. Wu (Eds.), KDD (pp. 350–359). ACM.

Lenca, P., Meyer, P., Vaillant, B., Lallich, S. (2008). On selecting interestingness measures for association rules: user oriented description and multiple criteria decision aid. European Journal of Operational Research, 184(2), 610–626.MATHCrossRef

Mampaey, M., & Vreeken, J. (2013). Summarizing categorical data by clustering attributes. Data Mining and Knowledge Discovery, 26(1), 130–173.MATHMathSciNetCrossRef

Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L. (1999). Discovering frequent closed itemsets for association rules. In C. Beeri, P. Buneman (Eds.), ICDT (pp. 398–416). Springer.

Peckham, J., & (Ed.) (1997). SIGMOD 1997, May 13–15. Tucson: ACM Press.

Pei, J., Han, J., Mao, R. (2000). Closet: an efficient algorithm for mining frequent closed itemsets. In ACM SIGMOD workshop on research issues in data mining and knowledge discovery (pp. 21–30).

Pei, Y., & Zaïane, O. (2006). A synthetic data generator for clustering and outlier analysis. Tech. rep.

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

Ramesh, G., Zaki, M.J., Maniatty, W. (2005). Distribution-based synthetic database generation techniques for itemset mining. In IDEAS (pp. 307–316). IEEE.

Tan, P.N., Kumar, V., Srivastava, J. (2002). Selecting the right interestingness measure for association patterns. In KDD (pp. 32–41). ACM.

Vaillant, B., Lenca, P., Lallich, S. (2004). A clustering of interestingness measures. In E. Suzuki, S. Arikawa (Eds.), Discovery science (pp. 290–297). Springer.

Vreeken, J., van Leeuwen, M., Siebes, A. (2007). Preserving privacy through data generation. In N. Ramakrishnan, O. Zaiane (Eds.), ICDM (pp. 685–690). IEEE.

Wu, T., Chen, Y., Han, J. (2010). Re-examination of interestingness measures in pattern mining: a unified framework. Data Mining and Knowledge Discovery, 21(3), 371–397.MathSciNetCrossRef

Zaki, M.J. (2000). Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering, 12(3), 372–390.MathSciNetCrossRef

Zaki, M.J., & Hsiao, C.J. (1999). Charm: an efficient algorithm for closed association rule mining. Tech. rep., CS Department, Rensselaer Polytech Institute.

Zaki, M.J., & Hsiao, C.J. (2002). Charm: an efficient algorithm for closed itemset mining. In Grossman, Han, Kumar, Mannila, Motwani (Eds.), SDM. SIAM.

Zheng, Z., Kohavi, R., Mason, L. (2001). Real world performance of association rule algorithms. In KDD (pp. 401–406).

Titel: Objectively evaluating condensed representations and interestingness measures for frequent itemset mining
verfasst von: Albrecht Zimmermann
Publikationsdatum: 01.12.2015
Verlag: Springer US
Erschienen in: Journal of Intelligent Information Systems / Ausgabe 3/2015
Print ISSN: 0925-9902
Elektronische ISSN: 1573-7675
DOI: https://doi.org/10.1007/s10844-013-0297-9

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