2015
DOI: 10.1155/2015/910281
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Research of Improved FP-Growth Algorithm in Association Rules Mining

Abstract: Association rules mining is an important technology in data mining. FP-Growth (frequent-pattern growth) algorithm is a classical algorithm in association rules mining. But the FP-Growth algorithm in mining needs two times to scan database, which reduces the efficiency of algorithm. Through the study of association rules mining and FP-Growth algorithm, we worked out improved algorithms of FP-Growth algorithm—Painting-Growth algorithm and N (not) Painting-Growth algorithm (removes the painting steps, and uses an… Show more

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Cited by 38 publications
(24 citation statements)
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“…It deals with the most important two drawbacks of traditional Apriori algorithm. The constructed FP tree is used for the development of traditional frequent pattern growth algorithm [7]. The FP Tree [9] is constructed based on the dataset using the two passes are as follows: Pass 1:…”
Section: Background Knowledge a Fp Growth Algorithmmentioning
confidence: 99%
See 1 more Smart Citation
“…It deals with the most important two drawbacks of traditional Apriori algorithm. The constructed FP tree is used for the development of traditional frequent pattern growth algorithm [7]. The FP Tree [9] is constructed based on the dataset using the two passes are as follows: Pass 1:…”
Section: Background Knowledge a Fp Growth Algorithmmentioning
confidence: 99%
“…al [6] were proposed optimized frequent pattern growth algorithm. It overcomes the existing frequent pattern growth algorithm [7] such as without generating huge number of conditional FP tree [9]. Moreover they only concentrates on the transactional databases and deals with the market itemsets.…”
mentioning
confidence: 99%
“…Confidence of an association rule denoted as , is an indication of how often the rule have been found to be true. It is given as Zeng et al [10] pointed out that for a given user-specified minimum support, minsup, if the itemset meets the condition then itemset is regarded as frequent itemset and conversely itemset is regarded as infrequent itemset.…”
Section: Support Of An Association Rulementioning
confidence: 99%
“…Classification is based on probabilistic substructure model: This method mainly consists of two algorithms: Apriori algorithm [3] and FP-Growth algorithm [4] . The core idea is to classify a complete subgraph model according to the support measure.…”
Section: Graph Classificationmentioning
confidence: 99%