SuffixMiner: efficiently mining frequent itemsets in data streams by suffix-forest

Authors:
Lifeng Jia;Chunguang Zhou;Zhe Wang;Xiujuan Xu
Affiliations:
Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science, Jilin University, Changchun, China;Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science, Jilin University, Changchun, China;Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science, Jilin University, Changchun, China;Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education, College of Computer Science, Jilin University, Changchun, China
Venue:
FSKD'05 Proceedings of the Second international conference on Fuzzy Systems and Knowledge Discovery - Volume Part II
Year:
2005

Citing 2
Cited 0

Fast Algorithms for Mining Association Rules in Large Databases

VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Approximate frequency counts over data streams

VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases

Quantified Score

Hi-index	0.00

Visualization

Abstract

We proposed a new algorithm SuffixMiner which eliminates the requirement of multiple passes through the data when finding out all frequent itemsets in data streams, takes full advantage of the special property of suffix-tree to avoid generating candidate itemsets and traversing each suffix-tree during the itemset growth, and utilizes a new itemset growth method to mine all frequent itemsets in data streams. Experiment results show that the SuffixMiner algorithm not only has an excellent scalability to mine frequent itemsets over data streams, but also outperforms Apriori and Fp-Growth algorithms.