Index-Maxminer: a New Maximal Frequent Itemset Mining Algorithm

Because of the inherent computational complexity, mining the complete frequent item-set in dense datasets remains to be a challenging task. Mining Maximal Frequent Item-set (MFI) is an alternative ...

[1]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[2]  Bart Goethals,et al.  Advances in Frequent Itemset Mining Implementations: Introduction to FIMI03 , 2003, FIMI.

[3]  Heikki Mannila,et al.  Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.

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

[5]  Bharat Bhasker,et al.  Efficiently mining Maximal Frequent Sets in dense databases for discovering association rules , 2004, Intell. Data Anal..

[6]  Zvi M. Kedem,et al.  Pincer-Search: A New Algorithm for Discovering the Maximum Frequent Set , 1998, EDBT.

[7]  Gösta Grahne,et al.  Reducing the Main Memory Consumptions of FPmax* and FPclose , 2004, FIMI.

[8]  Roberto J. Bayardo,et al.  Efficiently mining long patterns from databases , 1998, SIGMOD '98.

[9]  Dimitrios Gunopulos,et al.  Discovering All Most Specific Sentences by Randomized Algorithms , 1997, ICDT.

[10]  Johannes Gehrke,et al.  MAFIA: a maximal frequent itemset algorithm , 2005, IEEE Transactions on Knowledge and Data Engineering.

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

[12]  Ramesh C Agarwal,et al.  Depth first generation of long patterns , 2000, KDD '00.

[13]  Bharat Bhasker,et al.  Metamorphosis: Mining Maximal Frequent Sets in Dense Domains , 2005, Int. J. Artif. Intell. Tools.

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

[15]  Gösta Grahne,et al.  Fast algorithms for frequent itemset mining using FP-trees , 2005, IEEE Transactions on Knowledge and Data Engineering.

[16]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[17]  Wesley W. Chu,et al.  SmartMiner: a depth first algorithm guided by tail information for mining maximal frequent itemsets , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[18]  Aris Floratos,et al.  Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm [published erratum appears in Bioinformatics 1998;14(2): 229] , 1998, Bioinform..

[19]  Vijay V. Raghavan,et al.  Itemset Trees for Targeted Association Querying , 2003, IEEE Trans. Knowl. Data Eng..

[20]  Vipin Kumar,et al.  Scalable parallel data mining for association rules , 1997, SIGMOD '97.

[21]  Takashi Washio,et al.  An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data , 2000, PKDD.

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

[23]  Ron Rymon,et al.  Search through Systematic Set Enumeration , 1992, KR.

[24]  Yu Li,et al.  Searching for high-support itemsets in itemset trees , 2006, Intell. Data Anal..

[25]  Jian Pei,et al.  Mining frequent patterns without candidate generation , 2000, SIGMOD '00.

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

[27]  Charu C. Aggarwal,et al.  A Tree Projection Algorithm for Generation of Frequent Item Sets , 2001, J. Parallel Distributed Comput..