Application and improvement discussion about Apriori algorithm of association rules mining in cases mining of influenza treated by contemporary famous old Chinese medicine

Liu, Yuntao; Liu, Qing; Zheng, Danwen; Deng, Qingping; Tan, Zhanpeng; Jin, Xinchun; Huang, Wei; Lei, Yaling; Luo, Yi; Yin, Jian

doi:10.1109/bibmw.2012.6470323

Cited by 5 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And nowadays it is widely applied in various fields involving business, science and engineering, music, medicine. Liu et al [2] successfully discover the association rules by Apriori algorithm between the Etiology and TCM, Syndromes and TCM, Symptoms and TCM in influenza. Zhou et al [3] built a disease risk model of osteoporosis based on TCM symptoms and Western medicine risk factors with several data mining technique.…”

Section: Introductionmentioning

confidence: 99%

The application characteristics of traditional Chinese medical science treatment on headache based on data-mining Apriori algorithm

Wang

Zhang

et al. 2014

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

View full text Add to dashboard Cite

As the mechanism of the effective traditional Chinese medical science treatment remains confounding, researchers seek to analyze the therapies using traditional Chinese medicine (TCM) with advanced techniques. In this paper we take advantage of the technique of data mining with the software SPSS (Statistical Product and Service Solutions), in particular the Apriori algorithm for association rules mining (ARM), which discovers the following relationship by iteratively analyzing large datasets of TCM medical records. After analyzing the database, we are likely to reveal the mysterious interrelationships among TCM syndromes, clinical symptoms and curative Chinese herbal medicine. Here we list the principals, steps and pseudo-codes of Apriori algorithm, and then apply Apriori algorithm to analyze Chinese traditional medical cases of headache. Through verification, the Apriori algorithm proves effective in helping traditional Chinese doctors decide what to prescribe when facing various patients with headache. Finally we offer some improvement of Apriori algorithm for ARM to boom its effectiveness.

show abstract

Section: Introductionmentioning

confidence: 99%

The application characteristics of traditional Chinese medical science treatment on headache based on data-mining Apriori algorithm

Wang

Zhang

et al. 2014

2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

View full text Add to dashboard Cite

show abstract

“…Apriori algorithm seems to be the important and influential algorithm of the ARM (Gupta and Mamtora, 2014; Liu et al , 2012; Zhang and Du, 2011), which is applied for the frequent item set mining followed with the generation of the association rules. The item set available in the database is identified in order to frame the rules through an iterative method via a layer–layer search, and the computing is progressed through connection and pruning (Dongre et al , 2014; Liu et al , 2012). The term connection refers to the group of matrix functions, and the term pruning demolishes the intermediate outcomes.…”

Section: Introductionmentioning

confidence: 99%

Distributed elephant herding optimization for grid-based privacy association rule mining

Gopagoni

K²

2020

DTA

View full text Add to dashboard Cite

PurposeAssociation rule mining generates the patterns and correlations from the database, which requires large scanning time, and the cost of computation associated with the generation of the rules is quite high. On the other hand, the candidate rules generated using the traditional association rules mining face a huge challenge in terms of time and space, and the process is lengthy. In order to tackle the issues of the existing methods and to render the privacy rules, the paper proposes the grid-based privacy association rule mining.Design/methodology/approachThe primary intention of the research is to design and develop a distributed elephant herding optimization (EHO) for grid-based privacy association rule mining from the database. The proposed method of rule generation is processed as two steps: in the first step, the rules are generated using apriori algorithm, which is the effective association rule mining algorithm. In general, the extraction of the association rules from the input database is based on confidence and support that is replaced with new terms, such as probability-based confidence and holo-entropy. Thus, in the proposed model, the extraction of the association rules is based on probability-based confidence and holo-entropy. In the second step, the generated rules are given to the grid-based privacy rule mining, which produces privacy-dependent rules based on a novel optimization algorithm and grid-based fitness. The novel optimization algorithm is developed by integrating the distributed concept in EHO algorithm.FindingsThe experimentation of the method using the databases taken from the Frequent Itemset Mining Dataset Repository to prove the effectiveness of the distributed grid-based privacy association rule mining includes the retail, chess, T10I4D100K and T40I10D100K databases. The proposed method outperformed the existing methods through offering a higher degree of privacy and utility, and moreover, it is noted that the distributed nature of the association rule mining facilitates the parallel processing and generates the privacy rules without much computational burden. The rate of hiding capacity, the rate of information preservation and rate of the false rules generated for the proposed method are found to be 0.4468, 0.4488 and 0.0654, respectively, which is better compared with the existing rule mining methods.Originality/valueData mining is performed in a distributed manner through the grids that subdivide the input data, and the rules are framed using the apriori-based association mining, which is the modification of the standard apriori with the holo-entropy and probability-based confidence replacing the support and confidence in the standard apriori algorithm. The mined rules do not assure the privacy, and hence, the grid-based privacy rules are employed that utilize the adaptive elephant herding optimization (AEHO) for generating the privacy rules. The AEHO inherits the adaptive nature in the standard EHO, which renders the global optimal solution.

show abstract

“…The basic principles, processes, and algorithms for the Apriori algorithm of association rule mining were analyzed [17]. The inefficiencies and the problems of the basic Apriori algorithm, with respect to large datasets, were stated by the authors clearly.…”

Section: Review Of Existing Workmentioning

confidence: 99%

SOTARM: Size of transaction-based association rule mining algorithm

Pandian¹,

Thaveethu²

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:Mining of association rules tries to identify the existence of promising and fruitful relations among the items present in a database. The basic a priori algorithm suffers from multiple database scans, and if the database is large, then the time taken for scanning and generation of candidates is also large. The proposed algorithm attempts to reduce the repeated scanning of the whole database. Using this algorithm, scanning time and also the generation of subitems that are not frequent can be reduced. The former can be done by sorting the transaction records in descending order based on the size of transaction (SOT) and scanning only those transactions whose SOT is greater than or equal to k (size of item sets). The latter can be done by analyzing the item set state. It is not required to generate the next set of candidate item sets using those item sets that are not frequent. Both positive and negative mining has been done in R Studio of the R data mining tool using the R language. Experimental results show that the SOT algorithm performs better than the Apriori, Eclat, PVARM (partition-based validation for association rule mining), and NRRM (nonredundant rule method) algorithms. The work has been tested against various standard datasets such as Adult, Genome, Groceries, and SER (State Electricity Rate) Prediction. The speed-up and efficiency parameter values obtained from the algorithm strongly suggest that the proposed SOTARM algorithm has attained better performance when compared to all the other existing algorithms.

show abstract

Application and improvement discussion about Apriori algorithm of association rules mining in cases mining of influenza treated by contemporary famous old Chinese medicine

Cited by 5 publications

References 1 publication

The application characteristics of traditional Chinese medical science treatment on headache based on data-mining Apriori algorithm

The application characteristics of traditional Chinese medical science treatment on headache based on data-mining Apriori algorithm

Distributed elephant herding optimization for grid-based privacy association rule mining

SOTARM: Size of transaction-based association rule mining algorithm

Contact Info

Product

Resources

About