Short Time Association Rule Mining Algorithm

Many algorithms have been proposed to solve the problem of mining frequent itemset. The resulting frequent itemsets represent the global frequent patterns. This global output doesn't provide any information about the distribution of the frequent patterns on the database. This missing information can produce inaccurate decisions or prediction when the output frequent itemsets are used in decision support or prediction systems, specially, when the input database is non-uniformly distributed. In this work, we shall introduce a technique to calculate, store, and display frequent itemsets' distributions in the database. The proposed technique is called short time association rule mining (ST-ARM).

[1]  Laks V. S. Lakshmanan,et al.  Pushing Convertible Constraints in Frequent Itemset Mining , 2004, Data Mining and Knowledge Discovery.

[2]  Shamkant B. Navathe,et al.  An Efficient Algorithm for Mining Association Rules in Large Databases , 1995, VLDB.

[3]  Gösta Grahne,et al.  Efficiently Using Prefix-trees in Mining Frequent Itemsets , 2003, FIMI.

[4]  Johannes Gehrke,et al.  MAFIA: a maximal frequent itemset algorithm for transactional databases , 2001, Proceedings 17th International Conference on Data Engineering.

[5]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[6]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[7]  Zvi M. Kedem,et al.  Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set , 2002, IEEE Trans. Knowl. Data Eng..

[8]  Rajeev Motwani,et al.  Dynamic itemset counting and implication rules for market basket data , 1997, SIGMOD '97.

[9]  Jun-Lin Lin,et al.  Mining association rules: anti-skew algorithms , 1998, Proceedings 14th International Conference on Data Engineering.

[10]  Philippe Pucheral,et al.  Bitmap based algorithms for mining association rules , 1998, BDA.

[11]  Jian Pei,et al.  Mining frequent patterns by pattern-growth: methodology and implications , 2000, SKDD.

[12]  Ramakrishnan Srikant,et al.  Fast Algorithms for Mining Association Rules in Large Databases , 1994, VLDB.

[13]  Ramakrishnan Srikant,et al.  The Quest Data Mining System , 1996, KDD.

[14]  Laks V. S. Lakshmanan,et al.  Mining frequent itemsets with convertible constraints , 2001, Proceedings 17th International Conference on Data Engineering.

[15]  Mohammed J. Zaki.  Scalable Algorithms for Association Mining , 2000, IEEE Trans. Knowl. Data Eng..

[16]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[17]  Mohammed J. Zaki,et al.  Efficiently mining maximal frequent itemsets , 2001, Proceedings 2001 IEEE International Conference on Data Mining.