Distribution analysis of Pulmonary diseases in Traditional Chinese medicine based on FP-Growth algorithm

Tian, Shuanggui; Xiao, Yong; Shen, Shaowu

doi:10.1109/bibm49941.2020.9313583

Cited by 2 publications

(1 citation statement)

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“…The current algorithms specific to frequent itemset mining are largely divided into two major types: exact algorithms and heuristic algorithms. The most classical exact algorithms are the Apriori algorithm [10] and FP-Growth algorithm [11], as well as many improved algorithms derived from the two algorithms [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Right-Hand Side Expanding Algorithm for Maximal Frequent Itemset Mining

Zhang

Zhu

et al. 2021

Applied Sciences

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When it comes to association rule mining, all frequent itemsets are first found, and then the confidence level of association rules is calculated through the support degree of frequent itemsets. As all non-empty subsets in frequent itemsets are still frequent itemsets, all frequent itemsets can be acquired only by finding all maximal frequent itemsets (MFIs), whose supersets are not frequent itemsets. In this study, an algorithm, named right-hand side expanding (RHSE), which can accurately find all MFIs, was proposed. First, an Expanding Operation was designed, which, starting from any given frequent itemset, could add items using certain rules and form some supersets of given frequent itemsets. In addition, these supersets were all MFIs. Next, this operator was used to add items by taking all frequent 1-itemsets as the starting point alternately, and all MFIs were found in the end. Due to the special design of the Expanding Operation, each MFI could be found. Moreover, the path found was unique, which avoided the algorithm redundancy in temporal and spatial complexity. This algorithm, which has a high operating rate, is applicable to the big data of high-dimensional mass transactions as it is capable of avoiding the computing redundancy and finding all MFIs. In the end, a detailed experimental report on 10 open standard transaction sets was given in this study, including the big data calculation results of million-class transactions.

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