Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Algorithms for association rule mining — a general survey and comparison
ACM SIGKDD Explorations Newsletter
A lattice-based approach for I/O efficient association rule mining
Information Systems - Databases: Creation, management and utilization
Sequential PAttern mining using a bitmap representation
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Partitioning Large Data to Scale up Lattice-Based Algorithm
ICTAI '03 Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Demand-driven frequent itemset mining using pattern structures
Knowledge and Information Systems
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Frequent itemsets play an essential role in many data mining tasks that try to find interesting patterns from databases. A new algorithm based on the lattice theory and bitmap index for mining frequent itemsets is proposed in this paper. Firstly, the algorithm converts the origin transaction database to an itemsets-lattice (which is a directed graph) in the preprocessing, where each itemset vertex has a label to represent its support. So we can change the complicated task of mining frequent itessets in the database to a simpler one of searching vertexes in the lattice, which can speeds up greatly the mining process. Secondly, Support counting in the association rules mining requires a great I/O and computing cost. A bitmap index technique to speed up the counting process is employed in this paper. Saving the intact bitmap usually has a big space requirement. Each bit vector is partitioned into some blocks, and hence every bit block is encoded as a shorter symbol. Therefore the original bitmap is impacted efficiently. At the end experimental and analytical results are presented.