Agrawal, R,Srikant, R.Fast algorithms for mining association rules. In:Proceedings of the 20th International Conference on Very Large Databases (VLDB 1994; Santiago de, Chile).San Mateo, CA:Morgan Kaufmann;1994,487–499.

Agrawal, R,Mannila, H,Srikant, R,Toivonen, H,Verkamo, A.Fast discovery of association rules. In:Advances in Knowledge Discovery and Data Mining.Cambridge, CA: AAAI Press/MIT Press;1996,307–328.

Zaki, MJ,Parthasarathy, S,Ogihara, M,Li, W.New algorithms for fast discovery of association rules. In: Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining (*KDD 1997*; Newport Beach, CA). Menlo Park, CA: AAAI Press;1997,283–296.

Zaki, MJ,Gouda, K.Fast vertical mining using diffsets. In: Proceedings of the 9th ACM International Conference on Knowledge Discovery and Data Mining (*KDD 2003*; Washington, DC). New York: ACM Press;2003,326–335.

Schmidt‐Thieme, L.Algorithmic features of Eclat. In: Proceedings of the Workshop Frequent Item Set Mining Implementations (*FIMI 2004*; Brighton, UK). Aachen, Germany: CEUR Workshop Proceedings 126;2004.

Han, J,Pei, J,Yin, Y.Mining frequent patterns without candidate generation. In: Proceedings of the 19th ACM International Conference on Management of Data (*SIGMOD 2000*; Dallas, TX). New York, NY: ACM Press;2000,1–12.

Grahne, G,Zhu, J.Efficiently using prefix‐trees in mining frequent itemsets. In: Proceedings of the Workshop Frequent Item Set Mining Implementations (*FIMI 2003*; Melbourne, FL). Aachen, Germany: CEUR Workshop Proceedings 90;2003.

Rácz, B.Nonordfp: an FP‐growth variation without rebuilding the FP‐tree. In: Proceedings of the 2nd International Workshop on Frequent Itemset Mining Implementations (*FIMI 2004*; Brighton, UK). Aachen, Germany: CEUR Workshop Proceedings 126;2003.

Grahne, G,Zhu, J.Reducing the main memory consumptions of Fpmax* and FPclose. In: Proceedings of the Workshop Frequent Item Set Mining Implementations (*FIMI 2004*; Brighton, UK). Aachen, Germany: CEUR Workshop Proceedings 126;2004.

Uno, T,Asai, T,Uchida, Y,Arimura, H.LCM: an efficient algorithm for enumerating frequent closed item sets. In: Proceedings of the Workshop on Frequent Item Set Mining Implementations (*FIMI 2003*; Melbourne, FL). TU Aachen, Germany: CEUR Workshop Proceedings 90;2003.

Uno, T,Kiyomi, M,Arimura, H.LCM ver. 2: efficient mining algorithms for frequent/closed/maximal itemsets. In: Proceedings of the Workshop Frequent Item Set Mining Implementations (*FIMI 2004*; Brighton, UK). Aachen, Germany: CEUR Workshop Proceedings 126;2004.

Uno, T,Kiyomi, M,Arimura, H.LCM ver. 3: collaboration of array, bitmap and prefix tree for frequent itemset mining. In: Proceedings of the 1st Open Source Data Mining on Frequent Pattern Mining Implementations (*OSDM 2005*; Chicago, IL). New York, NY: ACM Press;2005,77–86.

Gerstein, GL,Perkel, DH,Subramanian, KN.Identification of functionally related neural assemblies.Brain Res1978,140:43–62.

Bayardo, RJ.Efficiently mining long patterns from databases. In: Proceedings of the ACM International Conference Management of Data (*SIGMOD 1998*; Seattle, WA). New York, NY: ACM Press;1998,85–93.

Lin, D‐I,Kedem, ZM.Pincer‐search: a new algorithm for discovering the maximum frequent set. In: Proceedings of the 6th International Conference on Extending Database Technology (*EDBT 1998*; Valencia, Spain). Heidelberg, Germany: Springer‐Verlag;1998,103–119.

Agrawal, RC,Aggarwal, CC,Prasad, VVV.Depth first generation of long patterns. In: Proceedings of the 6th ACM International Conference on Knowledge Discovery and Data Mining (*KDD 2000*; Boston, MA). New York, NY: ACM Press;2000,108–118.

Aggarwal, CC.Towards long pattern generation in dense databases.SIGKDD Explor2001,3:20–26.

Burdick, D,Calimlim, M,Gehrke, J.MAFIA: a maximal frequent itemset algorithm for transactional databases. In: Proceedings of the 17th International Conference on Data Engineering (*ICDE 2001*; Heidelberg, Germany). Piscataway, NJ: IEEE Press;2001,443–452.

Pasquier, N,Bastide, Y,Taouil, R,Lakhal, L.Discovering frequent closed itemsets for association rules. In: Proceedings of the 7th International Conference on Database Theory (*ICDT 1999*; Jerusalem, Israel). London, United Kingdom: Springer‐Verlag;1999,398–416.

Bastide, Y,Taouil, R,Pasquier, N,Stumme, G,Lakhal, L.Mining frequent patterns with counting inference.SIGKDD Explor2002,2:66–75.

Zaki, MJ.Generating non‐redundant association rules. In: Proceedings of the 6th ACM International Conference on Knowledge Discovery and Data Mining (*KDD 2000*, Boston, MA). New York, NY: ACM Press;2000,34–43.

Cristofor, D,Cristofor, L,Simovici, D.Galois connection and data mining.J Univ Comput Sci2000,6:60–73.

Pei, J,Han, J,Mao, R.Closet: an efficient algorithm for mining frequent closed itemsets. In: Proceedings of the SIGMOD International Workshop on Data Mining and Knowledge Discovery (*DMKD 2000*; Dallas, TX). ACM Press, New York, NY;2000,21–30.

Pan, F,Cong, G,Tung, AKH,Yang, J,Zaki, MJ.Carpenter: finding closed patterns in long biological datasets. In: Proceedings of the 9th ACM International Conference on Knowledge Discovery and Data Mining (*KDD 2003*; Washington, DC). New York, NY: ACM Press;2003,637–642.

Bastide, Y,Pasquier, N,Taouil, R,Stumme, G,Lakhal, L.Mining minimal non‐redundant association rules using frequent closed itemsets. In: Proceedings of the 1st International Conference on Computational Logic (*CL 2000*; London, UK). London, United Kingdom: Springer‐Verlag, 2000,972–986.

Bykowski, A,Rigotti, C.A condensed representation to find frequent patterns. In: Proceedings of the 20th ACM Symposium on Principles of Database Systems (*PODS 2001*; Santa Barbara, CA). New York, NY: ACM Press;2001,267–273.

Kryszkiewicz, M,Gajek, M.Concise representation of frequent patterns based on generalized disjunction‐free generators. In: Proceedings of the 6th Pacific‐Asia Conference on Knowledge Discovery and Data Mining (*PAKDD 2002*; Paipei, Taiwan). New York, NY: Springer‐Verlag;2002,159–171.

Liu, G,Li, J,Wong, L,Hsu, W.Positive borders or negative borders: how to make lossless generators based representations concise. In: Proceedings of the 6th SIAM International Conference on Data Mining (*SDM 2006*; Bethesda, MD). Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM);2006,469–473.

Liu, G,Li, J,Wong, L.A new concise representation of frequent itemsets using generators and a positive border.J Knowl Inf Syst2008,17:35–56.

Kohavi, R,Bradley, CE,Frasca, B,Mason, L,Zheng, Z.KDD‐Cup 2000 organizers’ report: peeling the onion.SIGKDD Explor2000,2:86–93.

Calders, T,Goethals, B.Mining all non‐derivable frequent itemsets. In: Proceedings of the 6th European Conference on Principles of Data Mining and Knowledge Discovery (*PKDD 2002*; Helsinki, Finland). Berlin, Germany: Springer;2002,74–85.

Muhonen, J,Toivonen, H.Closed non‐derivable itemsets. In: Proceedings of the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (*PKDD 2006*; Berlin, Germany). Berlin, Germany: Springer;2006,601–608.

Bodon, F.A fast apriori implementation. In: Proceedings of the Workshop on Frequent Item Set Mining Implementations (*FIMI 2003*; Melbourne, FL). TU Aachen, Germany: CEUR Workshop Proceedings 90;2003.

Borgelt, C.Efficient implementations of apriori and Eclat. In: Proceedings of the Workshop on Frequent Item Set Mining Implementations (*FIMI 2003*; Melbourne, FL). TU Aachen, Germany: CEUR Workshop Proceedings 90;2003.

Bodon, F.Surprising results of trie‐based fim algorithms. In: Proceedings of the 2nd Workshop Frequent Item Set Mining Implementations (*FIMI 2004*; Brighton, UK). Aachen, Germany: CEUR Workshop Proceedings 126;2004.

Borgelt, C.Recursion pruning for the apriori algorithm. In: Proceedings of the 2nd Workshop Frequent Item Set Mining Implementations (*FIMI 2004*; Brighton, UK). Aachen, Germany: CEUR Workshop Proceedings 126;2004.

Bodon, F,Schmidt‐Thieme, L.The relation of closed itemset mining, complete pruning strategies and item ordering in apriori‐based FIM algorithms. In: Proceedings of the 9th European Conference on Principles and Practice of Knowledge Discovery in Databases (*PKDD 2005*; Porto, Portugal). Berlin Germany: Springer‐Verlag;2005.

Kosters, WA,Pijls, W.Apriori: a depth first implementation. In: Proceedings of the Workshop on Frequent Item Set Mining Implementations (FIMI 2003; Melbourne, FL). TU Aachen, Germany: CEUR Workshop Proceedings 90;2003.

Wang, K,Tang, L,Han, J,Liu, J.Top‐down FP‐growth for association rule mining. In: Proceedings of the 6th Pacific‐Asia Conference on Advances in Knowledge Discovery and Data Mining (*PAKDD 2002*; Taipei, Taiwan). London, United Kingdom: Springer‐Verlag;2002,334–340.

Borgelt, C,Wang, X.SaM: a split and merge algorithm for fuzzy frequent item set mining. In: Proceedings of the 13th International Fuzzy Systems Association World Congress and 6th Conference of European Society for Fuzzy Logic and Technology (*IFSA/EUSFLAT’09*; Lisbon, Portugal). Lisbon, Portugal: IFSA/EUSFLAT Organization Committee;2009,968–973.

Rácz, B,Bodon, F,Schmidt‐Thieme, L.Benchmarking frequent itemset mining algorithms: from measurement to analysis. In: Proceedings of the 1st Open Source Data Mining on Frequent Pattern Mining Implementations (*OSDM 2005*, Chicago, IL). New York, NY: ACM Press;2005,36–45.

Bayardo, R,Goethals, B,Zaki, MJ, eds. In: Proceedings of the 2nd Workshop Frequent Item Set Mining Implementations (*FIMI 2004*; Brighton, UK). Aachen, Germany: CEUR Workshop Proceedings 126;2004.

Goethals, B,Zaki, MJ, eds. In: Proceedings of the Workshop Frequent Item Set Mining Implementations (*FIMI 2003*; Melbourne, FL). Aachen, Germany: CEUR Workshop Proceedings 90;2003.

Pietracaprina, A,Zandolin, D.Mining frequent itemsets using patricia tries. In: Proceedings of the Workshop on Frequent Item Set Mining Implementations (*FIMI 2003*; Melbourne, FL). TU Aachen, Germany: CEUR Workshop Proceedings 90;2003.

Schlegel, B,Gemulla, R,Lehner, W.Memory‐efficient frequent‐itemset mining. In: Proceedings of the 14th International Conference on Extending Database Technology (*EDBT 2011*; Uppsala, Sweden). New York, NY: ACM Press;2011,461–472.

Zaki, MJ,Hsiao, C‐J.CHARM: an efficient algorithm for closed itemset mining. In: Proceedings of the 2nd SIAM International Conference on Data Mining (*SDM 2002*; Arlington, VA). Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM);2002,457–473.

Wang, J,Han, J,Pei, J.Closet+: searching for the best strategies for mining frequent closed itemsets. In: Proceedings of the ACM International Conference on Knowledge Discovery and Data Mining (*KDD 2003*; Washington, DC). New York, NY: ACM Press;2003.

Lucchese, C,Orlando, S,Perego, R.DCI closed: a fast and memory efficient algorithm to mine frequent closed itemsets. In: Proceedings of the 2nd Workshop Frequent Item Set Mining Implementations (*FIMI 2004*; Brighton, UK). Aachen, Germany: CEUR Workshop Proceedings 126;2004.

Gouda, K,Zaki, MJ.Efficiently mining maximal frequent itemsets. In: Proceedings of the 1st IEEE International Conference on Data Mining (*ICDM 2001*; San Jose, CA). Piscataway, NJ: IEEE Press;2001,163–170.

Burdick, D,Calimlim, M,Flannick, J,Gehrke, J,Yiu, T.MAFIA: a performance study of mining maximal frequent itemsets. In: Proceedings of the Workshop on Frequent Item Set Mining Implementations (*FIMI 2003*; Melbourne, FL). TU Aachen, Germany: CEUR Workshop Proceedings 90;2003.

Mielikäinen, T.Intersecting data to closed sets with constraints. In: Proceedings of the Workshop Frequent Item Set Mining Implementations (*FIMI 2003*; Melbourne, FL). Aachen, Germany: CEUR Workshop Proceedings 90;2003.

Cong, G,Tan, KI,Tung, AKH,Pan, F.Mining frequent closed patterns in microarray data. In: Proceedings of the 4th IEEE International Conference on Data Mining (*ICDM 2004*; Brighton, UK). Piscataway, NJ: IEEE Press;2004,363–366.

Pan, F,Tung, AKH,Cong, G,Xu, X.Cobbler: combining column and row enumeration for closed pattern discovery. In: Proceedings of the 16th International Conference on Scientific and Statistical Database Management (*SSDBM 2004*; Santori Island, Greece). Piscataway, NJ: IEEE Press;2004,21–30.

Ganter, B,Wille, R.Formal Concept Analysis: Mathematical Foundations.Berlin: Springer,1999.

Rioult, F,Boulicaut, J‐F,Crémilleux, B,Besson, J.Using transposition for pattern discovery from microarray data. In: Proceedings of the 8th ACMSIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (*DMKD2003*; San Diego, CA). New York, NY: ACM Press;2003,73–79.

Borgelt, C,Yang, X,Nogales‐Cadenas, R,Carmona‐Saez, P,Pascual‐Montano, A.Finding closed frequent item sets by intersecting transactions. In: Proceedings of the 14th International Conference on Extending Database Technology (*EDBT 2011*; Uppsala, Sweden). New York, NY: ACM Press;2011,367–376.

Creighton, C,Hanash, S.Mining gene expression databases for association rules.Bioinformatics2003,19:79–86.

Agrawal, R,Imielienski, T,Swami, A.Mining association rules between sets of items in large databases. In: Proceedings of the ACM International Conference on Management of Data (*SIGMOD 1993*; Washington, DC). New York, NY: ACM Press;1993,207–216.

Srikant, R,Agrawal, R.Mining generalized association rules. In: Proceedings of the 21st International Conference on Very Large Databases (*VLDB 1995*; Zurich, Switzerland). San Mateo, CA: Morgan Kaufmann;1995,407–419.

Srikant, R,Agrawal, R.Mining quantitative association rules in large relational tables. In: Proceedings of the ACM International Conference on Management of Data (*SIGMOD 1996*; Montreal, Canada). New York, NY: ACM Press;1996,1–12.

Kuok, C,Fu, A,Wong, M.Mining fuzzy association rules in databases.SIGMOD Rec1998,27:41–46.

Brin, S,Motwani, R,Ullman, JD,Tsur, S.Dynamic itemset counting and implication rules for market basket data. In: Proceedings of the ACM International Conference on Management of Data (*SIGMOD 1997*; Tucson, AZ). New York, NY: ACM Press;1997,265–276.

Piatetsky‐Shapiro, G.Discovery, analysis, and presentation of strong rules. In:Piatetsky‐Shapiro, G,Frawley, WJ, eds.Knowledge Discovery in Databases.Palo Alto, CA: AAAI Press;1991,229–248.

Tan, P‐N,Kumar, V,Srivastava, J.Selecting the right interestingness measure for association patterns. In: Proceedings of the 8th ACM International Conference on Knowledge Discovery and Data Mining (*KDD 2002*; Edmonton, Canada). New York, NY: ACM Press;2002,32–41.

Tan, P‐N,Kumar, V,Srivastava, J.Selecting the right objective measure for association analysis.Inf Syst2004,29:293–313.

Geng, L,Hamilton, HJ.Interestingness measures for data mining: a survey.ACM Comput Surv (CSUR)2006,38:Article9.

Webb, GI,Zhang, S.*k*‐Optimal‐rule‐discovery.Data Min Knowl Discov2005,10:39–79.

Webb, GI.Discovering significant patterns.Mach Learn2007,68:1–33.

Choi, S‐S,Cha, S‐H,Tappert, CC.A survey of binary similarity and distance measures.J Syst Cybern Inf2010,8:43–48.

Segond, M,Borgelt, C.Item set mining based on cover similarity. In: Proceedings of the 15th Pacific‐Asia Conference on Knowledge Discovery and Data Mining (*PAKDD 2011*; Shenzhen, China). Berlin, Germany: Springer‐Verlag;2011, LNCS 6635:493–505.

Jaccard, P.Étude comparative de la distribution florale dans une portion des Alpes et des Jura.Bulletin de la Société Vaudoise des Sciences Naturelles1991;37:547–579. France 1901.

Seno, M,Karypis, G.LPMiner: an algorithm for finding frequent itemsets using length decreasing support constraint. In: Proceedings of the 1st IEEE International Conference on Data Mining (*ICDM 2001*; San Jose, CA). Piscataway, NJ: IEEE Press;2001,505–512.

Wang, J,Karypis, G.BAMBOO: accelerating closed itemset mining by deeply pushing the length‐decreasing support constraint. In: Proceedings of the SIAM International Conference on Data Mining (*SDM 2004;* Disneyworld, FL). Philadelphia, PA: Society for Industrial and Applied Mathematics;2004,432–436.

Geerts, F,Goethals, B,Mielikäinen, T.Tiling databases. In: Proceedings of the 7th International Conference on Discovery Science (*DS 2004*; Padova, Italy). Berlin, Germany: Springer;2004,278–289.

Bonferroni, CE.Il calcolo delle assicurazioni su gruppi di teste.Studi in Onore del Professore Salvatore Ortu Carboni1935,13–60.

Abdi, H.Bonferroni and Sidák corrections for multiple comparisons. In:Salkind, NJ, ed.Encyclopedia of Measurement and Statistics.Thousand Oaks, CA: Sage Publications;2007.

Holm, S.A simple sequentially rejective multiple test procedure.Scand J Stat1979,6:65–70.

Webb, GI.Layered critical values: a powerful direct adjustment approach to discovering significant patterns.Mach Learn2008,71:307–323.

Megiddo, N,Srikant, R.Discovering predictive association rules. In: Proceedings of the 4th International Conference on Knowledge Discovery and Data Mining (*KDD 1998*; New York, NY). Menlo Park, CA: AAAI Press;1998,27–78.

Gionis, A,Mannila, H,Mielikäinen, T,Tsaparas, P.Assessing data mining results via swap randomization. In: Proceedings of the 12th ACM International Conference on Knowledge Discovery and Data Mining (*KDD 2006*; Philadelphia, PA). New York, NY: ACM Press;2006,167–176.

Siebes, A,Vreeken, J,van Leeuwen, M.Item sets that compress. In: Proceedings of the SIAM International Conference on Data Mining (*SDM 2006*; Bethesda, MD). Philadelphia, PA: Society for Industrial and Applied Mathematics;2006,393–404.

Vreeken, J,van Leeuwen, M,Siebes, A.Krimp: mining itemsets that compress.Data Min Knowl Discov2011,23:169–214.

Bringmann, B,Zimmermann, A.The chosen few: on identifying valuable patterns. In: Proceedings of the 7th IEEE International Conference on Data Mining (*ICDM 2007*; Omaha, NE). Piscataway, NJ: IEEE Press;2007,63–72.

De Raedt, L,Zimmermann, A.Constraint‐based pattern set mining. In: Proceedings of the 7th IEEE International Conference on Data Mining (*ICDM 2007*; Omaha, NE). Piscataway, NJ: IEEE Press;2007,237–248.

Webb, GI.Self‐sufficient itemsets: an approach to screening potentially interesting associations between items.ACM Trans Knowl Discov Data (TKDD)2010,4:Article3.

Cheng, H,Yu, PS,Han, J.Approximate frequent itemset mining in the presence of random noise. In:Maimon, O,Rokach, L, eds.Soft Computing for Knowledge Discovery and Data Mining. Vol.IV.Berlin: Springer;2008,363–389.

Pei, J,Tung, AKH,Han, J.Fault‐tolerant frequent pattern mining: problems and challenges. In: Proceedings of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (*DMKD 2001*; Santa Babara, CA). New York, NY: ACM Press;2001.

Besson, J,Robardet, C,Boulicaut, J‐F.Mining a new fault‐tolerant pattern type as an alternative to formal concept discovery. In: Proceedings of the International Conference on Computational Science (*ICCS 2006*; Reading, United Kingdom). Berlin, Germany: Springer‐Verlag;2006,144–157.

Gionis, A,Mannila, H,Seppänen, JK.Geometric and combinatorial tiles in 0‐1 data. In: Proceedings of the 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (*PKDD 2004*; Pisa, Italy). Berlin, Germany: Springer‐Verlag;2004, LNAI 3202:173–184.

Yang, C,Fayyad, U,Bradley, PS.Efficient discovery of error‐tolerant frequent itemsets in high dimensions. In: Proceedings of the 7th ACM International Conference on Knowledge Discovery and Data Mining (*KDD 2001*; San Francisco, CA). New York, NY: ACM Press;2001,194–203.

Seppänen, JK,Mannila, H.Dense itemsets. In: Proceedings of the 10th ACM International Conference on Knowledge Discovery and Data Mining (*KDD 2004*; Seattle, WA). New York, NY: ACM Press;2004,683–688.

Liu, J,Paulsen, S,Sun, X,Wang, W,Nobel, A,Prins, J.Mining approximate frequent itemsets in the presence of noise: algorithm and analysis. In: Proceedings of the 6th SIAM Conference on Data Mining (*SDM 2006*; Bethesda, MD). Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM);2006,405–416.

Wang, X,Borgelt, C,Kruse, R.Mining fuzzy frequent item sets. In: Proceedings of the 11th International Fuzzy Systems Association World Congress (*IFSA 2005*; Beijing, China). Beijing, China; Heidelberg, Germany: Tsinghua University Press; Springer‐Verlag;2005,528–533.

Chui, C‐K,Kao, B,Hung, E.Mining frequent itemsets from uncertain data. In: Proceedings of the 11th Pacific‐Asia Conference on Knowledge Discovery and Data Mining (*PAKDD 2007*; Nanjing, China). Berlin, Germany: Springer‐Verlag;2007,47–58.

Leung, CK‐S,Carmichael, CL,Hao, B.Efficient mining of frequent patterns from uncertain data. In: Proceedings of the 7th IEEE International Conference on Data Mining Workshops (*ICDMW 2007*; Omaha, NE). Piscataway, NJ: IEEE Press;2007,489–494.

Aggarwal, CC,Lin, Y,Wang, J,Wang, J.Frequent pattern mining with uncertain data. In: Proceedings of the 15th ACM International Conference on Knowledge Discovery and Data Mining (*KDD 2009*; Paris, France). New York, NY: ACM Press;2009,29–38.

Calders, T,Garboni, C,Goethals, B.Efficient pattern mining of uncertain data with sampling. In: Proceedings of the 14th Pacific‐Asia Conference on Knowledge Discovery and Data Mining (*PAKDD 2010*; Hyderabad, India). Berlin, Germany: Springer‐Verlag;2010, I:480–487.

Calders, T,Garboni, C,Goethals, B.Approximation of frequentness probability of itemsets in uncertain data. In: Proceedings of the IEEE International Conference on Data Mining (*ICDM 2010*; Sydney, Australia). Piscataway, NJ: IEEE Press;2010,749–754.