A Comparative Study of FP-growth Variations

Summary Finding frequent itemsets in databases is crucial in data mining for purpose of extracting association rules. Many algorithms were developed to find those frequent itemsets. This paper presents a summarization and a comparative study of the available FP-growth algorithm variations produced for mining frequent itemsets showing their capabilities and efficiency in terms of time and memory consumption.

[1]  Jian Pei,et al.  Data Mining: Concepts and Techniques, 3rd edition , 2006 .

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

[3]  Balázs Rácz,et al.  nonordfp: An FP-growth variation without rebuilding the FP-tree , 2004, FIMI.

[4]  Petra Perner,et al.  Data Mining - Concepts and Techniques , 2002, Künstliche Intell..

[5]  Jun Gao.  Realization of a New Association Rule Mining Algorithm , 2007 .

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

[7]  Jun Gao.  Realization of a New Association Rule Mining Algorithm , 2007, 2007 International Conference on Computational Intelligence and Security (CIS 2007).

[8]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

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

[10]  Qiming Chen,et al.  PrefixSpan,: mining sequential patterns efficiently by prefix-projected pattern growth , 2001, Proceedings 17th International Conference on Data Engineering.

[11]  Michael Stonebraker,et al.  The Morgan Kaufmann Series in Data Management Systems , 1999 .

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

[13]  Hongjun Lu,et al.  AFOPT: An Efficient Implementation of Pattern Growth Approach , 2003, FIMI.