Mining maximal frequent itemsets: a java implementation of FPMAX algorithm

Authors:
B. Ziani;Y. Ouinten
Affiliations:
Department of computer science, Laghouat University, Algeria;Department of computer science, Laghouat University, Algeria
Venue:
IIT'09 Proceedings of the 6th international conference on Innovations in information technology
Year:
2009

Citing 4
Cited 1

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Mining frequent patterns without candidate generation

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MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases

Proceedings of the 17th 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 improved approach for automatic selection of multi-tables indexes in ralational data warehouses using maximal frequent itemsets

Intelligent Decision Technologies

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Abstract

Mining maximal frequent itemsets is an important issue in many data mining applications. In our thesis work on selection and tuning of indices in data werhouses, we have proposed a strategy based on mining maximal frequent itemsets in order to determine a set of candidate indices from a given workload. In a first step we have to select an algorithm, for mining maximal frequent itemsets, to implement. Experimental results in the repository of the workshops on Frequent Itemset Mining Implementations (http://fimi.cs.helsinki.fi/), shows that FPMAX has the best performance. Therefore, we have selected it for our own implementation in java language. FPMAX is an extension of FP-Growth method for mining maximal frequent itemsets only. We tested our implementation on two benchmark databases MUSHROOM and RETAIL. We compare our results with the best implementations available in the repository mentioned earlier. Our implementation showed good performances compared with the others. However, the comparison of response times published in FIMI 2004, for the chosen implementations, could not be replicated.