scholarly journals Association analysis in food sampling inspection data

2022 ◽  
Vol 355 ◽  
pp. 02033
Author(s):  
Tongqiang Jiang ◽  
Xin Chen ◽  
Huan Jiang
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Strong Association ◽  
Apriori Algorithm ◽  
Work Efficiency ◽  
Food Category ◽  
Sampling Inspection ◽  
Inspection Data ◽  
The Cost

At present, China exists a problem that the cost of food sampling inspection is too high. This paper attempts to reduce the number of sampling inspection items in the same food category, reduce the cost of food sampling inspection, and improve the work efficiency through the association analysis of national sampling inspection data. And this paper applies Apriori algorithm to analyse the association rules, which is based on the unqualified pastry sampling inspection data in the 2019 national food sampling inspection database. Finally, we obtain 10 strong association rules through experiments. The results show that this association analysis can reduce the workload of food sampling inspection effectively.

scholarly journals The water supply association analysis method in Shenzhen based on kmeans clustering discretization and apriori algorithm

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255684
Author(s):  
Xin Liu ◽  
Xuefeng Sang ◽  
Jiaxuan Chang ◽  
Yang Zheng ◽  
Yuping Han
Keyword(s):  
Water Supply ◽  
Association Analysis ◽  
Association Rules ◽  
Strong Association ◽  
Continuous Data ◽  
Apriori Algorithm ◽  
Analysis Method ◽  
Attribution Analysis ◽  
The One ◽  
Data Discretization

Since water supply association analysis plays an important role in attribution analysis of water supply fluctuation, how to carry out effective association analysis has become a critical problem. However, the current techniques and methods used for association analysis are not very effective because they are based on continuous data. In general, there is different degrees of monotone relationship between continuous data, which makes the analysis results easily affected by monotone relationship. The multicollinearity between continuous data distorts these analytical methods and may generate incorrect results. Meanwhile, we cannot know the association rules and value interval between features and water supply. Therefore, the lack of an effective analysis method hinders the water supply association analysis. Association rules and value interval of features obtained from association analysis are helpful to grasp cause of water supply fluctuation and know the fluctuation interval of water supply, so as to provide better support for water supply dispatching. But the association rules and value interval between features and water supply are not fully understood. In this study, a data mining method coupling kmeans clustering discretization and apriori algorithm was proposed. The kmeans was used for data discretization to obtain the one-hot encoding that can be recognized by apriori, and the discretization can also avoid the influence of monotone relationship and multicollinearity on analysis results. All the rules eventually need to be validated in order to filter out spurious rules. The results show that the method in this study is an effective association analysis method. The method can not only obtain the valid strong association rules between features and water supply, but also understand whether the association relationship between features and water supply is direct or indirect. Meanwhile, the method can also obtain value interval of features, the association degree between features and confidence probability of rules.

Research and Application of Apriori Algorithm for Mining Association Rules

2014 ◽  
Vol 1079-1080 ◽  
pp. 737-742
Author(s):  
Yi Yong Ye
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Web Mining ◽  
Recommendation System ◽  
Apriori Algorithm ◽  
Mining Algorithm ◽  
Common Technique ◽  
Mining Association Rules

For large amounts of data generated by the e-commerceplatform, combining with the actual needs of e-commerce recommendation system,make research on a common technique of association rules which orientede-commerce Web mining association analysis, introduces the association rules ofApriori mining algorithm, and the specific application of Apriori algorithm isanalyzed through a practical example, Finally, point out the shortcomings ofclassical Apriori algorithm, and gives directions for improvement.

scholarly journals Defect Data Association Analysis of the Secondary System Based on AFWA-H-Mine

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4228
Author(s):  
Yan Xu ◽  
Mingyu Wang ◽  
Wen Fan
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Weighted Average ◽  
Screening Strategy ◽  
Apriori Algorithm ◽  
Secondary System ◽  
Fireworks Algorithm ◽  
Confidence Threshold ◽  
Support Threshold ◽  
Data Coverage

The fault data of the secondary system of smart substations hide some information that the association analysis algorithm can mine. The convergence speed of the Apriori algorithm and FP-growth algorithm is slow, and there is a lack of indicators to evaluate the correlation of association rules and the method to determine the parameter threshold. In this paper, the H-mine algorithm is used to realize the fast mining of fault data. The algorithm can traverse data faster by using the data structure of the H-struct. This paper also sets the lift and CF value to screen the association rules with good correlation. When setting the three key parameters of association analysis, namely, support threshold, confidence threshold, and lift threshold, an objective function composed of weighted average lift, CF value, and data coverage rate was selected, and the adaptive fireworks algorithm was used to optimize the parameters in the association analysis. In particular, the rule screening strategy is introduced in fault cause analysis in this paper. By eliminating rules with high similarity, derived signals in association rules are eliminated to the greatest extent to improve the readability of rules and ensure easy understanding of results.

The Research of Improving Clothing Crossing-Selling Based on Apriori Arithmetic

2014 ◽  
Vol 989-994 ◽  
pp. 1586-1589
Author(s):  
Chi Zhang ◽  
Yi Liu ◽  
Fang Shuai Sun ◽  
Li Yao
Keyword(s):  
Association Analysis ◽  
Association Rules ◽  
Apriori Algorithm ◽  
Commercial Test ◽  
Sales Data ◽  
Clothing Store ◽  
Specific Association

First, we describe crossing-selling and association rules. And then with the study of a correlation clothing store sales data, it shows the Apriori algorithm applies in specific association analysis. We can propose a model which is suitable for crossing-selling. Through commercial test, the algorithm can significantly increase sales of the relevant product.

Application of Apriori Algorithm in Diagnosis of Stroke

2012 ◽  
Vol 490-495 ◽  
pp. 2017-2021
Author(s):  
Wei Peng Zhang
Keyword(s):  
Association Rules ◽  
Blood Viscosity ◽  
Whole Blood ◽  
Medical Information ◽  
Strong Association ◽  
Plasma Viscosity ◽  
Apriori Algorithm ◽  
Whole Blood Viscosity ◽  
Blood Sedimentation ◽  
Subjective Interestingness

To apply Apriori algorithm to analysis on association of stroke and hemorheology, and obtain the meaningful medical information. A large number of hemorheology data of patients with stroke were collected, including whole blood viscosity low cut, whole blood viscosity medium cut, whole blood viscosity high cut, blood sedimentation, hematocrit, plasma viscosity, thrombus, age, sex. Minimum support was 0.2 and minimum confidence was 0.8 as experience for analysis of association rules with apriori algorithm. Four strong association rules were screened by the objective and subjective interestingness, which contained the relation between the stroke and age, sex, whole blood viscosity, plasma viscosity. The results show that Apriori algorithm can be used to study the the diagnosis and prevention of stroke.

Improved Apriori Algorithm in the Power System Fault

2014 ◽  
Vol 556-562 ◽  
pp. 1510-1514
Author(s):  
Li Qiang Lin ◽  
Hong Wen Yan
Keyword(s):  
Power System ◽  
Association Rules ◽  
Fault Analysis ◽  
The Other ◽  
Apriori Algorithm ◽  
Extreme Climate ◽  
Time Space ◽  
Low Efficiency ◽  
Candidate Item ◽  
Improved Algorithm

For the low efficiency in generating candidate item sets of apriori algorithm, this paper presents a method based on property division to improve generating candidate item sets. Comparing the improved apriori algorithm with the other algorithm and the improved algorithm is applied to the power system accident cases in extreme climate. The experiment results show that the improved algorithm significantly improves the time efficiency of generating candidate item sets. And it can find the association rules among time, space, disasters and fault facilities in the power system accident cases in extreme climate. That is very useful in power system fault analysis.

scholarly journals MINING ESSENTIAL RULES USING FREQUENT CLOSED ITEMSETS LATTICE

2009 ◽  
Vol 12 (11) ◽  
pp. 49-56
Author(s):  
Bac Hoai Le ◽  
Bay Dinh Vo
Keyword(s):  
Association Rules ◽  
Frequent Itemsets ◽  
Suitable Method ◽  
Mining Method ◽  
Parent Child Relationship ◽  
Left Hand ◽  
Child Relationship ◽  
Closed Itemsets ◽  
The Cost ◽  
Parent Child

In traditional mining of association rules, finding all association rules from databases that satisfy minSup and minConf faces with some problems in case of the number of frequent itemsets is large. Thus, it is necessary to have a suitable method for mining fewer rules but they still embrace all rules of traditional mining method. One of the approaches that is the mining method of essential rules: it only keeps the rule that its left hand side is minimal and its right side is maximal (follow in parent-child relationship). In this paper, we propose a new algorithm for mining the essential rules from the frequent closed itemsets lattice to reduce the time of mining rules. We use the parent-child relationship in lattice to reduce the cost of considering parent-child relationship and lead to reduce the time of mining rules.

scholarly journals ANALISIS POLA PENJUALAN DENGAN MENGGUNAKAN ALGORITMA APRIORI PADA KOPERASI KARYAWAN YAYASAN ANAKKU

2019 ◽  
Vol 2 (1) ◽  
pp. 31-36
Author(s):  
Arfianto Darmawan ◽  
Titin Kristiana
Keyword(s):  
Data Mining ◽  
Strong Association ◽  
Apriori Algorithm ◽  
Business Unit ◽  
Savings And Loans ◽  
Transaction Data ◽  
Existing Problems ◽  
A Value ◽  
Transaction Database ◽  
Using Data

The Anakku Foundation Cooperative is a multi-business cooperative consisting of shop businesses, savings and loans, and student shuttle services. Every sale of stuff services will be inputted data directly to each business unit. The Anakku Foundation Cooperative still has problems, including store transactions that cannot yet answer what items are often sold, when stock items are still difficult to determine the items that are still available or almost running out. Data mining techniques have been mostly used to overcome existing problems, one of which is the application of the Apriori algorithm to obtain information about the associations between products from a transaction database. Transaction data on school equipment sales at Cooperative Employees of Anakku Foundation can be reprocessed using Data mining applications so as to produce strong association rules between itemset sales of school supplies so that they can provide recommendations for item alignment and simplify the arrangement or strong item placement related to interdependence. The results are found that the highest value of support and confidence is if buying MUSLIM L1.5P1, so it would buy AL-IZHAR II LOGO with a value of 14.5% support and 79.5% confidence

scholarly journals IMPLEMENTASI ALGORITMA APRIORI TERHADAP DATA PENJUALAN PADA PERUSAHAAN RETAIL

2019 ◽  
Vol 15 (1) ◽  
pp. 85-90 ◽  
Author(s):  
Jordy Lasmana Putra ◽  
Mugi Raharjo ◽  
Tommi Alfian Armawan Sandi ◽  
Ridwan Ridwan ◽  
Rizal Prasetyo
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
A Priori ◽  
Apriori Algorithm ◽  
Business World ◽  
Transaction Data ◽  
Retail Company ◽  
Special Strategy

The development of the business world is increasingly rapid, so it needs a special strategy to increase the turnover of the company, in this case the retail company. In increasing the company's turnover can be done using the Data Mining process, one of which is using apriori algorithm. With a priori algorithm can be found association rules which can later be used as patterns of purchasing goods by consumers, this study uses a repository of 209 records consisting of 23 transactions and 164 attributes. From the results of this study, the goods with the name CREAM CUPID HEART COAT HANGER are the products most often purchased by consumers. By knowing the pattern of purchasing goods by consumers, the company management can increase the company's turnover by referring to the results of processing sales transaction data using a priori algorithm

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