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 another way to achieve). We compared two kinds of improved algorithms with FP-Growth algorithm. Experimental results show that Painting-Growth algorithm is more than 1050 and N Painting-Growth algorithm is less than 10000 in data volume; the performance of the two kinds of improved algorithms is better than that of FP-Growth algorithm.

Improved Event Data Scheduling Framework Through Optimized Fp-Growth Algorithm

“…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:…”

“…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.…”

Mining Frequent Patterns of Crime using FP-Growth with Multiple Minimum Supports based on Shannon Entropy

“…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.…”

Current Situation and Application of Graph Data Mining Technology

“…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.…”