Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Generating Frequent Patterns with the Frequent Pattern List
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
Efficient Algorithms for Mining Closed Itemsets and Their Lattice Structure
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
DBV-Miner: A Dynamic Bit-Vector approach for fast mining frequent closed itemsets
Expert Systems with Applications: An International Journal
Mining frequent itemsets in large databases: The hierarchical partitioning approach
Expert Systems with Applications: An International Journal
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The mining of frequent itemsets is a fundamental and important task of data mining. To improve the efficiency in mining frequent itemsets, many researchers developed smart data structures to represent the database, and designed divide-and-conquers approaches to generate frequent itemsets from these data structures. However, the features of real databases are diversified and the features of local databases in the mining process may also change. Consequently, different data structures may be utilized in the mining process to enhance efficiency. This study presents an adaptive mechanism to select suitable data structures depending on database densities: the Frequent Pattern List (FPL) for sparse databases, and the Transaction Pattern List (TPL) for dense databases. Experimental results verified the effectiveness of this approach.