Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 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
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Using a Hash-Based Method with Transaction Trimming for Mining Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on 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
An adaptive approach to mining frequent itemsets efficiently
Expert Systems with Applications: An International Journal
Mining frequent itemsets in large databases: The hierarchical partitioning approach
Expert Systems with Applications: An International Journal
An Empirical Investigation into the Sources of Customer Dissatisfaction with Online Games
International Journal of E-Business Research
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The generation of frequent patterns (or frequent itemsets) has been studied in various areas of data mining. Most of the studies take the Apriori-based generation-and-test approach, which is computationally costly in the generation of candidate frequent patterns. Methods like frequent pattern trees has been utilized to avoid candidate set generation, but they work with more complicated data structures. In this paper, we propose another approach to mining frequent patterns without candidate generation. Our approach uses a simple linear list called Frequent Pattern List (FPL). By performing simple operations on FPLs, we can discover frequent patterns easily. Two algorithms, FPL-Construction and FPL-Mining, are proposed to construct the FPL and generate frequent patterns from the FPL, respectively.