Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Beyond market baskets: generalizing association rules to correlations
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Efficiently mining long patterns from databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Efficient mining of association rules using closed itemset lattices
Information Systems
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Efficient Mining of Partial Periodic Patterns in Time Series Database
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Models for association rules based on clustering and correlation
Intelligent Data Analysis
Cluster analysis on time series gene expression data
International Journal of Business Intelligence and Data Mining
Algorithms for mining frequent itemsets in static and dynamic datasets
Intelligent Data Analysis
Evaluating association rules and decision trees to predict multiple target attributes
Intelligent Data Analysis
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The generation of frequent itemsets is an essential and time-consuming step in mining association rules. Most of the studies adopt the Apriori-based approach, which has great effort in generating candidate itemsets and needs multiple database accesses. Recent studies indicate that FP-tree approach has been utilized to avoid the generation of candidate itemsets and scan transaction database only twice, but they work with more complicated data structure. Besides, it needs to adjust the structure of FP-tree when it applied to incremental mining application. It is necessary to adjust the position of an item upward or downward in the structure of FP-tree when a new transaction increases or decreases the accumulation of the item. The process of the adjustment of the structure of FP-tree is the bottlenecks of the FP-tree in incremental mining application. Therefore, algorithms for efficient mining of frequent patterns are in urgent demand. This paper aims to improve both time and space efficiency in mining frequent itemsets and incremental mining application. We propose a novel QSD (Quick Simple Decomposition) algorithm using simple decompose principle which derived from minimal heap tree, we can discover the frequent itemsets quickly under one database scan. Meanwhile, QSD algorithm doesn't need to scan database and reconstruct data structure again when database is updated or minimum support is varied. It can be applied to on-line incremental mining applications without any modification. Comprehensive experiments have been conducted to assess the performance of the proposed algorithm. The experimental results show that the QSD algorithm outperforms previous algorithms.