Frequent Itemset Mining Based on Bit-Sequence

Ren, Jiadong; Yi, Juan; He, Hong

doi:10.1007/978-3-642-26001-8_77

Cited by 1 publication

(1 citation statement)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Among them, the number of occurrences of some candidate set is very small, but also has to scan the entire transaction set, which have a huge impact on the algorithm of space and time efficiency. Therefore, the improved Apriori algorithms mainly optimize the statistical of number of itemsets, such as hash-based technology [4], the transaction compression [21], sampling [8], and so on.…”

Section: Analysis Of Apriori Algorithmmentioning

confidence: 99%

Research on Frequent Itemsets Mining Algorithm based on Relational Database

Wang¹,

Wang²,

Zhang³

et al. 2013

JSW

View full text Add to dashboard Cite

Mining association rules between items is an important research direction of data mining, and the relational database is the most popular database, so mining association rules in the relational database is a very important research direction. At present, neither the Apriori algorithm nor its improvements resolve some problems generating candidate itemset and scanning the transaction set repeatedly, which lead to low efficiency. This paper proposes the frequent itemsets mining algorithm based on relational database based on the study of those important mining association rules algorithms and the storage characteristics of the transaction set and items in the relational database, and presents its concrete implementation and its optimization method. This algorithm combines items in a transaction to generate itemsets and counts the same itemsets in all transactions, which improve the efficiency of execution. Moreover, this algorithm doesn’t produce candidate itemsets, and only scans transaction database once, so promotes considerably efficiency. The result of experiments shows that, the frequent itemsets mining algorithm based on relational database has higher efficiency than the classical Apriori algorithm under certain conditions.

show abstract

Section: Analysis Of Apriori Algorithmmentioning

confidence: 99%