Towards long pattern generation in dense databases
- 1 July 2001
- journal article
- Published by Association for Computing Machinery (ACM) in ACM SIGKDD Explorations Newsletter
- Vol. 3 (1) , 20-26
- https://doi.org/10.1145/507533.507537
Abstract
This paper discusses the problem of long pattern generation in dense databases. In recent years, there has been an increase of interest in techniques for maximal pattern generation. We present a survey of this class of methods for long pattern generation which differ considerably from the level-wise approach of traditional methods. Many of these techniques are rooted in combinatorial tricks which can be applied only when the generation of frequent patterns is not forced to be level wise. We present an overview of the different kinds of methods which can be used in order to improve the counting and search space exploration methods for long patterns.Keywords
This publication has 11 references indexed in Scilit:
- A Tree Projection Algorithm for Generation of Frequent Item SetsJournal of Parallel and Distributed Computing, 2001
- FreeSpanPublished by Association for Computing Machinery (ACM) ,2000
- Depth first generation of long patternsPublished by Association for Computing Machinery (ACM) ,2000
- Turbo-charging vertical mining of large databasesPublished by Association for Computing Machinery (ACM) ,2000
- Mining frequent patterns without candidate generationPublished by Association for Computing Machinery (ACM) ,2000
- Scalable algorithms for association miningIEEE Transactions on Knowledge and Data Engineering, 2000
- Efficiently mining long patterns from databasesPublished by Association for Computing Machinery (ACM) ,1998
- Combinatorial pattern discovery in biological sequences: The TEIRESIAS algorithm.Bioinformatics, 1998
- Dynamic itemset counting and implication rules for market basket dataPublished by Association for Computing Machinery (ACM) ,1997
- Mining association rules between sets of items in large databasesPublished by Association for Computing Machinery (ACM) ,1993