Abstract
The key point of this article is that, in frequent pattern mining, the most appropriate way of exploiting monotone constraints in conjunction with frequency is to use them in order to reduce the input data; this reduction in turn induces a stronger pruning of the search space of the problem. Following this intuition, we introduce ExAMiner, a breadth-first algorithm that exploits the real synergy of antimonotone and monotone constraints: the total benefit is greater than the sum of the two individual benefits. ExAMiner generalizes the basic idea of the preprocessing algorithm ExAnte (Bonchi et al. 2003(b)), embedding such ideas at all levels of an Apriori-like computation. The resulting algorithm is the generalization of the Apriori algorithm when a conjunction of monotone constraints is conjoined to the frequency antimonotone constraint. Experimental results confirm that this is, so far, the most efficient way of attacking the computational problem in analysis.
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References
Agrawal R, Srikant R (1994) (1994) Fast algorithms for mining association rules in large databases. In: Proceedings of the twentieth international conference on very large databases. Santiago de Chile, Chile, September 1994, pp 487–499
Agrawal R, Imielinski T, Swami A (1993) Mining associations between sets of items in massive databases. In: Proceedings of the ACM SIGMOD international conference on management of data. Washington DC, May 1993, pp 207–216
Bonchi F, Giannotti F, Mazzanti A, Pedreschi D (2003) Adaptive constraint pushing in frequent pattern mining. In: Proceedings of the 7th European conference on principles and practice of knowledge discovery in databases. Cavtat-Dubrovnik, Croatia, September 2003. (Lecture notes in computer science), vol 2838. Springer, Berlin Heidelberg New York, pp 47–58
Bonchi F, Giannotti F, Mazzanti A, Pedreschi D (2003) ExAnte: anticipated data reduction in constrained pattern mining. In: Proceedings of the 7th European conference on principles and practice of knowledge discovery in databases. Cavtat-Dubrovnik, Croatia, September 2003. (Lecture notes in computer science), vol 2838. Springer, Berlin Heidelberg New York, pp 59–70
Boulicaut JF, Jeudy B (2002) Optimization of association rule mining queries. Intell Data Anal J 6:341–357
Bucila C, Gehrke J, Kifer D, White W (2002) DualMiner: a dual-pruning algorithm for itemsets with constraints. In: Proceedings of the eighth ACM SIGKDD international conference on knowledge discovery and data mining. July 2002, Edmonton, Alberta, Canada, pp 42–51
De Raedt L, Kramer S (2001) The levelwise version space algorithm and its application to molecular fragment finding. In: Proceedings of the seventeenth international joint conference on artificial intelligence, Seattle, Washington, August 2001, pp 853–862
Grahne G, Lakshmanan L, Wang X (2000) Efficient mining of constrained correlated sets. In: Proceedings of the 16th IEEE international conference on data engineering, March 2000, San Diego, California, pp 512–524
Han J, Lakshmanan L, Ng R (1999) Constraint-based, multidimensional data mining. Computer 32:46–50
Inokuchi A, Washio T, Motoda H (2000) An Apriori-based algorithm for mining frequent substructures from graph data. In: Proceedings of the 4th European conference on principles and practice of knowledge discovery in databases. Lyon, France, September 2000 (Lecture notes in computer science), vol 1910. Springer, Berlin Heidelberg New York, pp 13–23
Kuramochi M, Karypis G (2001) Frequent subgraph discovery. In: Proceedings of the 2001 IEEE international conference on data mining. December 2001, San Jose, California, pp 313–320
Lakshmanan L, Ng R, Han J, Pang A (1999) Optimization of constrained frequent set queries with 2-variable constraints. In: Proceedings ACM SIGMOD international conference on management of data. June 1999, Philadelphia, Pennsylvania, pp 157–168
Ng R, Lakshmanan L, Han J, Pang A (1998) exploratory mining and pruning optimizations of constrained associations rules. In: Proceedings ACM SIGMOD international conference on management of data. June 1998, Seattle, Washington, pp 13–24
Orlando S, Palmerini P, Perego R, Silvestri F (2002) Adaptive and resource-aware mining of frequent sets. In: Proceedings of the 2002 IEEE international conference on data mining. December 2002, Maebashi City, Japan, pp 338–345
Park JS, Chen MS, Yu PS (1995) An effective hash based algorithm for mining association rules. In: Proceedings ACM SIGMOD international conference on management of data. May 1995, San Jose, California, pp 175–186
Pei J, Han J (2000) Can we push more constraints into frequent pattern mining? In: Proceedings ACM SIGKDD international conference on knowledge discovery and data mining. August 2000, Boston, MA, pp 350–354
Pei J, Han J, Lakshmanan L (2001) Mining frequent item sets with convertible constraints. In: Proceedings of the 17th IEEE international conference on data engineering. April 2001, Heidelberg, Germany, pp 433–442
Srikant R, Vu Q, Agrawal R (1997) Mining association rules with item constraints. In: Proceedings ACM SIGKDD international conference on knowledge discovery and data mining. August 1997, Newport Beach, California, pp 67–73
Zheng Z, Kohavi R, Mason L (2001) Real world performance of association rule algorithms. In: Proceedings ACM SIGKDD international conference on knowledge discovery and data mining. August 2001, San Francisco, California, pp 401–406
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Bonchi, F., Giannotti, F., Mazzanti, A. et al. Efficient breadth-first mining of frequent pattern with monotone constraints. Knowl Inf Syst 8, 131–153 (2005). https://doi.org/10.1007/s10115-004-0164-7
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DOI: https://doi.org/10.1007/s10115-004-0164-7