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Data Mining and Constraints: An Overview

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Data Mining and Constraint Programming

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10101))

Abstract

This paper provides an overview of the current state-of-the-art on using constraints in knowledge discovery and data mining. The use of constraints requires mechanisms for defining and evaluating them during the knowledge extraction process. We give a structured account of three main groups of constraints based on the specific context in which they are defined and used. The aim is to provide a complete view on constraints as a building block of data mining methods.

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References

  1. Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: Optics: ordering points to identify the clustering structure. In: Proceedings of the 1999 ACM SIGMOD International Conference on Management of Data, SIGMOD 1999, pp. 49–60. ACM, New York, NY, USA (1999)

    Google Scholar 

  2. Aizerman, M.A., Braverman, E.A., Rozonoer, L.: Theoretical foundations of the potential function method in pattern recognition learning. Autom. Remote Control 25, 821–837 (1964)

    MathSciNet  Google Scholar 

  3. Ayres, J., Flannick, J., Gehrke, J., Yiu, T.: Sequential pattern mining using a bitmap representation. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 429–435 (2002)

    Google Scholar 

  4. Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases (VLDB 1994), Santiago de Chile, Chile, 12–15 September, pp. 487–499 (1994)

    Google Scholar 

  5. Ahmed, C.F., Tanbeer, S.K., Jeong, B.-S., Lee, Y.-K., Choi, H.-J.: Single-pass incremental and interactive mining for weighted frequent patterns. Expert Syst. Appl. 39(9), 7976–7994 (2012)

    Article  Google Scholar 

  6. Bradley, P.S., Bennett, K.P., Demiriz, A.: Constrained k-means clustering. Technical report, MSR-TR-2000-65, Microsoft Research (2000)

    Google Scholar 

  7. Basu, S., Bilenko, M., Mooney, R.J.: A probabilistic framework for semi-supervised clustering. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 59–68 (2004)

    Google Scholar 

  8. Bilenko, M., Basu, S., Mooney, R.J.: Integrating constraints and metric learning in semi-supervised clustering. In: Proceedings of the Twenty-First International Conference on Machine Learning, ICML 2004, p. 11. ACM, New York (2004)

    Google Scholar 

  9. Baralis, E., Cagliero, L., Cerquitelli, T., Garza, P.: Generalized association rule mining with constraints. Inf. Sci. 194, 68–84 (2012)

    Article  Google Scholar 

  10. Basu, S., Davidson, I., Wagstaff, K.L.: Constrained Clustering: Advances in Algorithms, Theory, and Applications. Chapman and Hall/CRC, Boca Raton (2008)

    MATH  Google Scholar 

  11. Bertsekas, D.P.: Linear Network Optimization - Algorithms and Codes. MIT Press, Cambridge (1991)

    MATH  Google Scholar 

  12. Breiman, L., Friedman, J., Olshen, R., Stone, C.: Classification and Regression Trees. Wadsworth International Group, Belmont (1984)

    MATH  Google Scholar 

  13. Banerjee, A., Ghosh, J.: Scalable clustering algorithms with balancing constraints. Data Min. Knowl. Discov. 13(3), 365–395 (2006)

    Article  MathSciNet  Google Scholar 

  14. Banerjee, A., Ghosh, J.: Clustering with balancing constraints. Constrained Clustering: Advances in Algorithms. Theory, and Applications, pp. 171–200. Chapman and Hall/CRC, Boca Raton (2008)

    Google Scholar 

  15. Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In Proceedings of the Fifth Annual Workshop on Computational Learning Theory, COLT 1992, pp. 144–152. ACM, New York (1992)

    Google Scholar 

  16. Barbará, D., Kamath, C. (eds.): Proceedings of the Third SIAM International Conference on Data Mining, 1–3 May 2003. SIAM, San Francisco (2003)

    Google Scholar 

  17. Bistarelli, S., Montanari, U., Rossi, F.: Semiring-based constraint solving and optimization. J. ACM 44(2), 201–236 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  18. Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Discov. 2(2), 121–167 (1998)

    Article  Google Scholar 

  19. Bult, J.R., Wansbeek, T.J.: Optimal selection for direct mail. Mark. Sci. 14(4), 378–394 (1995)

    Article  Google Scholar 

  20. Capelle, M., Masson, C., Boulicaut, J.F.: Mining frequent sequential patterns under regular expressions: a highly adaptive strategy for pushing constraints. In: Proceedings of the Third SIAM International Conference on Data Mining, pp. 316–320 (2003)

    Google Scholar 

  21. Chand, C., Thakkar, A., Ganatra, A.: Sequential pattern mining: survey and current research challenges. Int. J. Soft Comput. Eng. (IJSCE) 2(1), 2231–2307 (2012)

    Google Scholar 

  22. Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)

    MATH  Google Scholar 

  23. Davidson, I.: Two approaches to understanding when constraints help clustering. In: The 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 1312–1320 (2012)

    Google Scholar 

  24. Demiriz, A., Bennett, K.P., Bradley, P.S.: Using assignment constraints to avoid empty clusters in k-means clustering. Constrained Clustering: Advances in Algorithms. Theory, and Applications, pp. 201–220. Chapman and Hall/CRC, Boca Raton (2008)

    Google Scholar 

  25. Dao, T.-B.-H., Duong, K.-C., Vrain, C.: A declarative framework for constrained clustering. In: Blockeel, H., Kersting, K., Nijssen, S., Železný, F. (eds.) ECML PKDD 2013. LNCS (LNAI), vol. 8190, pp. 419–434. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40994-3_27

    Chapter  Google Scholar 

  26. Duda, R.O., Hart, P.E.: Pattern Classification and Scene Analysis. Wiley, New York (1973)

    MATH  Google Scholar 

  27. Druck, G., Mann, G.S., McCallum, A.: Learning from labeled features using generalized expectation criteria. In: Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), pp. 595–602 (2008)

    Google Scholar 

  28. Domingos, P., Pazzani, M.J.: Beyond independence: conditions for the optimality of the simple Bayesian classifier. In: Proceedings of the 13th International Conference on Machine Learning (ICML 1996), Bari, Italy, pp. 148–156 (1996)

    Google Scholar 

  29. Davidson, I., Ravi, S.S.: Clustering with constraints: feasibility issues and the k-means algorithm. In: Proceedings of the SIAM International Conference on Data Mining (SDM) (2005)

    Google Scholar 

  30. Davidson, I., Ravi, S.S.: Identifying and generating easy sets of constraints for clustering. In: Proceedings of the Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference (AAAI), pp. 336–341 (2006)

    Google Scholar 

  31. Davidson, I., Ravi, S.S.: The complexity of non-hierarchical clustering with instance and cluster level constraints. Data Min. Knowl. Discov. 14(1), 25–61 (2007)

    Article  MathSciNet  Google Scholar 

  32. Davidson, I., Ravi, S.S.: Using instance-level constraints in agglomerative hierarchical clustering: theoretical and empirical results. Data Min. Knowl. Discov. 18(2), 257–282 (2009)

    Article  MathSciNet  Google Scholar 

  33. Davidson, I., Wagstaff, K.L., Basu, S.: Measuring constraint-set utility for partitional clustering algorithms. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 115–126. Springer, Heidelberg (2006). doi:10.1007/11871637_15

    Chapter  Google Scholar 

  34. Ester, M., Kriegel, H.-P., Sander, J., Xiaowei, X.: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD), pp. 226–231 (1996)

    Google Scholar 

  35. Elloumi, M., Zomaya, A.Y.: Biological Knowledge Discovery Handbook: Preprocessing, Mining and Postprocessing of Biological Data, 1st edn. Wiley, New York (2013)

    Book  MATH  Google Scholar 

  36. Yongjian, F., Han, J.: Meta-rule-guided mining of association rules in relational databases. In: Proceedings of the Post-Conference Workshops on Integration of Knowledge Discovery in Databases with Deductive and Object-Oriented Databases (KDOOD/TDOOD), pp. 39–46 (1995)

    Google Scholar 

  37. Grossi, V., Monreale, A., Nanni, M., Pedreschi, D., Turini, F.: Clustering formulation using constraint optimization. In: Bianculli, D., Calinescu, R., Rumpe, B. (eds.) SEFM 2015. LNCS, vol. 9509, pp. 93–107. Springer, Heidelberg (2015). doi:10.1007/978-3-662-49224-6_9

    Chapter  Google Scholar 

  38. Garofalakis, M.N., Rastogi, R., Shim, K.: SPIRIT: Sequential pattern mining with regular expression constraints. In: Proceedings of 25th International Conference on Very Large Data Bases (VLDB), pp. 223–234 (1999)

    Google Scholar 

  39. Grossi, V., Sperduti, A.: Kernel-based selective ensemble learning for streams of trees. In: Walsh, T. (ed.) IJCAI 2011, Proceedings of the 22nd International Joint Conference on Artificial Intelligence, Barcelona, Catalonia, Spain, 16–22 July 2011, pp. 1281–1287. IJCAI/AAAI (2011)

    Google Scholar 

  40. Grossi, V., Turini, F.: Stream mining: a novel architecture for ensemble-based classification. Knowl. Inf. Syst. 30(2), 247–281 (2012)

    Article  Google Scholar 

  41. Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Discov. 15(1), 55–86 (2007)

    Article  MathSciNet  Google Scholar 

  42. Han, J., Fu, Y.: Mining multiple-level association rules in large databases. IEEE Trans. Knowl. Data Eng. 11(5), 798–805 (1999)

    Article  Google Scholar 

  43. Han, J., Kamber, M.: Data Mining: Concepts and Techniques, 2nd edn. Morgan Kaufmann, San Francisco (2012)

    MATH  Google Scholar 

  44. Han, J., Lakshmanan, L.V.S., Ng, R.T.: Constraint-based multidimensional data mining. IEEE Comput. 32(8), 46–50 (1999)

    Article  Google Scholar 

  45. Har-Peled, S., Roth, D., Zimak, D.: Constraint classification: a new approach to multiclass classification. In: Proceedings of the 13th International Conference Algorithmic Learning Theory (ALT), pp. 365–379 (2002)

    Google Scholar 

  46. Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, Dallas, Texas, USA, 16–18 May, pp. 1–12 (2000)

    Google Scholar 

  47. Inokuchi, A., Washio, T., Motoda, H.: An apriori-based algorithm for mining frequent substructures from graph data. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000). doi:10.1007/3-540-45372-5_2

    Chapter  Google Scholar 

  48. Jensen, F.V.: An introduction to Bayesian networks. Springer, New York (1996)

    Google Scholar 

  49. Kumar, N., Kummamuru, K.: Semisupervised clustering with metric learning using relative comparisons. IEEE Trans. Knowl. Data Eng. 20(4), 496–503 (2008)

    Article  Google Scholar 

  50. Kummamuru, K., Krishnapuram, R., Agrawal, R.: Learning spatially variant dissimilarity (SVaD) measures. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 611–616 (2004)

    Google Scholar 

  51. Lin, T.S., Loh, W.Y., Shib, Y.S.: A comparison of prediction accuracy, complexity, and training time of thirty-tree old and new classification algorithms. Mach. Learn. 40(3), 203–228 (2000)

    Article  Google Scholar 

  52. Li, Y.-C., Yeh, J.-S., Chang, C.-C.: Isolated items discarding strategy for discovering high utility itemsets. Data Knowl. Eng. 64(1), 198–217 (2008)

    Article  Google Scholar 

  53. Mehta, M., Agrawal, R., Rissanen, J.: SLIQ: A fast scalable classifier for data mining. In: Proceedings of 5th International Conference on Extending Database Technology (EBDT 1996), Avignon, France, pp. 18–32 (1996)

    Google Scholar 

  54. Mabroukeh, N.R., Ezeife, C.I.: A taxonomy of sequential pattern mining algorithms. ACM Comput. Surv. 43(1), 3: 1–3: 41 (2010)

    Article  Google Scholar 

  55. Michell, T.: Machine Learning. McGraw Hill, New York (1997)

    Google Scholar 

  56. Moret, B.M.E.: Decision trees and diagrams. Comput. Surv. 14(4), 593–623 (1982)

    Article  Google Scholar 

  57. Masseglia, F., Poncelet, P., Teisseire, M.: Efficient mining of sequential patterns with time constraints: reducing the combinations. Expert Syst. Appl. 36(2), 2677–2690 (2009)

    Article  Google Scholar 

  58. Nijssen, S., Fromont, É.: Mining optimal decision trees from itemset lattices. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD), pp. 530–539 (2007)

    Google Scholar 

  59. Nijssen, S., Fromont, E.: Optimal constraint-based decision tree induction from itemset lattices. Data Min. Knowl. Discov. Fromont. 21(1), 9–51 (2010)

    Article  MathSciNet  Google Scholar 

  60. Niyogi, P., Pierrot, J.-B., Siohan, O.: Multiple classifiers by constrained minimization. In: Proceedings of the Acoustics, Speech, and Signal Processing of 2000 IEEE International Conference on ICASSP 2000, vol. 06, pp. 3462–3465. IEEE Computer Society, Washington, DC (2000)

    Google Scholar 

  61. Okabe, M., Yamada, S.: Clustering by learning constraints priorities. In: Proceedings of the 12th International Conference on Data Mining (ICDM2012), pp. 1050–1055 (2012)

    Google Scholar 

  62. Park, S.H., Furnkranz, J.: Multi-label classification with label constraints. Technical report, Knowledge Engineering Group, TU Darmstadt (2008)

    Google Scholar 

  63. Pei, J., Han, J., Mortazavi-Asl, B., Wang, J., Pinto, H., Chen, Q., Dayal, U., Hsu, M.: Mining sequential patterns by pattern-growth: the prefixspan approach. IEEE Trans. Knowl. Data Eng. 16(11), 1424–1440 (2004)

    Article  Google Scholar 

  64. Pei, J., Han, J., Wang, W.: Constraint-based sequential pattern mining: the pattern growth methods. J. Intell. Inf. Syst. 28(2), 133–160 (2007)

    Article  Google Scholar 

  65. Pyle, D.: Data Preparation for Data Mining. Morgan Kaufmann Publishers Inc., San Francisco (1999)

    Google Scholar 

  66. Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1, 81–106 (1986)

    Google Scholar 

  67. Quinlan, J.R.: C4.5 Programs for Machine Learning. Wadsworth International Group, Belmont (1993)

    Google Scholar 

  68. Quinlan, J.R.: Improved use of continuous attributes in C4.5. J. Artif. Intell. Res. 4, 77–90 (1996)

    MATH  Google Scholar 

  69. Ruggieri, S.: Efficient C4.5. IEEE Trans. Knowl. Data Eng. 14(2), 438–444 (2002)

    Article  Google Scholar 

  70. Srikant, R., Agrawal, R.: Mining generalized association rules. In: Proceedings of the 21st Conference on Very Large Data Bases (VLDB), pp. 407–419 (1995)

    Google Scholar 

  71. Srikant, R., Agrawal, R.: Mining sequential patterns: generalizations and performance improvements. In: Proceedings of the 5th International Conference on Extending Database Technology (EDBT), pp. 3–17 (1996)

    Google Scholar 

  72. Shafer, J., Agrawal, R., Mehta, M.: Sprint: a scalable parallel classifier for data mining. In: Proceedings of 1996 International Conference on Very Large Data Bases (VLDB 1996), Bombay, India, pp. 544–555 (1996)

    Google Scholar 

  73. Steinwart, I., Christmann, A.: Support Vector Machines, 1st edn. Springer Publishing Company, Incorporated, Heidelberg (2008)

    MATH  Google Scholar 

  74. Strehl, A., Ghosh, J.: Relationship-based clustering and visualization for high-dimensional data mining. INFORMS J. Comput. 15(2), 208–230 (2003)

    Article  MATH  Google Scholar 

  75. Shankar, S.: Utility sentient frequent itemset mining and association rule mining: a literature survey and comparative study. Int. J. Soft Comput. Appl. 4, 81–95 (2009)

    Google Scholar 

  76. Schultz, M., Joachims, T.: Learning a distance metric from relative comparisons. In: Proceedings of Conference Advances in Neural Information Processing Systems (NIPS) (2003)

    Google Scholar 

  77. Schultz, M., Joachims, T.: Learning a distance metric from relative comparisons. In: NIPS, MIT Press (2004)

    Google Scholar 

  78. Savasere, A., Omiecinski, E., Navathe, S.B.: An efficient algorithm for mining association rules in large databases. In: Proceedings of the 21st International Conference on Very Large Data Bases (VLDB), Zurich, Switzerland, 11–15 September 1995, pp. 432–444 (1995)

    Google Scholar 

  79. Sriphaew, K., Theeramunkong, T.: A new method for finding generalized frequent itemsets in generalized association rule mining. In: Proceedings of the 7th IEEE Symposium on Computers and Communications (ISCC), pp. 1040–1045 (2002)

    Google Scholar 

  80. Srikant, R., Quoc, V., Agrawal, R.: Mining association rules with item constraints. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD), pp. 67–73 (1997)

    Google Scholar 

  81. Tsochantaridis, I., Joachims, T., Hofmann, T., Altun, Y.: Large margin methods for structured and interdependent output variables. J. Mach. Learn. Res. 6, 1453–1484 (2005)

    MathSciNet  MATH  Google Scholar 

  82. Tao, F., Murtagh, F.: Weighted association rule mining using weighted support and significance framework. In: Proceedings of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 661–666 (2003)

    Google Scholar 

  83. Toivonen, H.: Sampling large databases for association rules. In: Proceedings of the 22nd International Conference on Very Large Data Bases (VLDB), Mumbai (Bombay), India, 3–6 September, pp. 134–145 (1996)

    Google Scholar 

  84. Tan, P.N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Addison Wesley, Boston (2006)

    Google Scholar 

  85. Tseng, V.S., Shie, B.-E., Wu, C.-W., Philip, S.: Efficient algorithms for mining high utility itemsets from transactional databases. IEEE Transactions on Knowledge and Data Engineering, forthcoming

    Google Scholar 

  86. Vanderlooy, S., Sprinkhuizen-Kuyper, I.G., Smirnov, E.N., Jaap van den Herik, H.: The ROC isometrics approach to construct reliable classifiers. Intell. Data Anal. 13(1), 3–37 (2009)

    Google Scholar 

  87. Wagstaff, K., Basu, S., Davidson, I.: When is constrained clustering beneficial, and why? In: Proceedings of The Twenty-First National Conference on Artificial Intelligence and the Eighteenth Innovative Applications of Artificial Intelligence Conference (AAAI) (2006)

    Google Scholar 

  88. Wagstaff, K., Cardie, C.: Clustering with instance-level constraints. In: Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on on Innovative Applications of Artificial Intelligence (AAAI/IAAI), p. 1097 (2000)

    Google Scholar 

  89. Wagstaff, K., Cardie, C., Rogers, S., Schrödl, S.: Constrained k-means clustering with background knowledge. In: Proceedings of the Eighteenth International Conference on Machine Learning, ICML 2001, pp. 577–584. Morgan Kaufmann Publishers Inc., San Francisco (2001)

    Google Scholar 

  90. Witten, I.H., Frank, E., Hall, M.: Data Mining, Pratical Machine Learning Tools and Techiniques, 3rd edn. Morgan Kaufmann, San Francisco (2011)

    Google Scholar 

  91. Wu, C.-M., Huang, Y.-F.: Generalized association rule mining using an efficient data structure. Expert Syst. Appl. 38(6), 7277–7290 (2011)

    Article  Google Scholar 

  92. Weiss, S.M., Kulikowski, C.A.: Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets. Machine Learning and Expert Systems. Morgan Kaufmann, San Francisco (1991)

    Google Scholar 

  93. Wei, J.-T., Lin, S.-Y., Hsin-Hung, W.: A review of the application of RFM model. Afr. J. Bus. Manag. 4(19), 4199–4206 (2010)

    Google Scholar 

  94. Wang, W., Wang, C., Zhu, Y., Shi, B., Pei, J., Yan, X., Han, J.: Graphminer: a structural pattern-mining system for large disk-based graph databases and its applications. In: zcan, F. (ed.) SIGMOD Conference, pp. 879–881. ACM (2005)

    Google Scholar 

  95. Wang, W., Yang, J., Philip, S.: Efficient mining of weighted association rules (WAR). In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 270–274 (2000)

    Google Scholar 

  96. Yan, W., Goebel, K.F.: Designing classifier ensembles with constrained performance requirements. In: Proceedings of SPIE Defense and Security Symposium, Multisensor Multisource Information Fusion: Architectures, Algorithms, and Applications 2004, pp. 78–87 (2004)

    Google Scholar 

  97. Yan, X., Han, J.: gSpan: Graph-based substructure pattern mining. In: Proceedings of the 2002 IEEE International Conference on Data Mining, ICDM 2002, p. 721. IEEE Computer Society, Washington, DC, USA (2002)

    Google Scholar 

  98. Yun, U., Leggett, J.J.: WFIM: weighted frequent itemset mining with a weight range and a minimum weight. In: Proceeding of the 2005 SIAM International Data Mining Conference, Newport Beach, CA, pp. 636–640 (2005)

    Google Scholar 

  99. Yun, U., HoRyu, K.: Approximate weighted frequent pattern mining with/without noisy environments. Knowl.-Based Syst. 24(1), 73–82 (2011)

    Article  Google Scholar 

  100. Yun, U., Shin, H., Ho Ryu, K., Yoon, E.: An efficient mining algorithm for maximal weighted frequent patterns in transactional databases. Knowl.-Based Syst. 33, 53–64 (2012)

    Article  Google Scholar 

  101. Zaki, M.J.: SPADE: an efficient algorithm for mining frequent sequences. Mach. Learn. 42(1/2), 31–60 (2001)

    Article  MATH  Google Scholar 

  102. Zhong, S., Ghosh, J.: Scalable, balanced model-based clustering. In: Proceedings of the Third SIAM International Conference on Data Mining, San Francisco (SDM) (2003)

    Google Scholar 

  103. Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New algorithms for fast discovery of association rules. In: Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD 1997), Newport Beach, California, USA, 14–17 August, pp. 283–286 (1997)

    Google Scholar 

  104. Zhang, T., Ramakrishnan, R., Livny, M.: Birch: an efficient data clustering method for very large databases. SIGMOD Rec. 25(2), 103–114 (1996)

    Article  Google Scholar 

  105. Zhang, C., Zhang, S.: Association Rule Mining: Models and Algorithms. LNCS, vol. 2307. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  106. Zhang, Y., Zhang, L., Nie, G., Shi, Y.: A survey of interestingness measures for association rules. In: Proceedings of the Second International Conference on Business Intelligence and Financial Engineering (BIFE), pp. 460–463 (2009)

    Google Scholar 

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  1. Department of Computer Science, University of Pisa, Largo B. Pontecorvo, 3, 56127, Pisa, Italy

    Valerio Grossi, Dino Pedreschi & Franco Turini

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  1. Valerio Grossi
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  2. Dino Pedreschi
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Correspondence to Valerio Grossi .

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Editors and Affiliations

  1. Université Montpellier 2, Montpellier, France

    Christian Bessiere

  2. KU Leuven, Heverlee, Belgium

    Luc De Raedt

  3. University of British Columbia, Vancouver, British Columbia, Canada

    Lars Kotthoff

  4. Université Catholique de Louvain, Louvain-la-Neuve, Belgium

    Siegfried Nijssen

  5. University College Cork, Cork, Ireland

    Barry O'Sullivan

  6. University of Pisa, Pisa, Italy

    Dino Pedreschi

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Grossi, V., Pedreschi, D., Turini, F. (2016). Data Mining and Constraints: An Overview. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_2

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