Association Rules Extraction from Date’s Product Dataset Using the Apriori Algorithm

2021 ◽  
pp. 241-248
Author(s):  
Jorge Diaz ◽  
David Ovallos-Gazabon ◽  
Carlos Vargas Mercado
Keyword(s):  
Association Rules ◽  
Apriori Algorithm ◽  
Rules Extraction

scholarly journals Association Rules Extraction for Customer Segmentation in the SMEs Sector Using the Apriori Algorithm

2019 ◽  
Vol 151 ◽  
pp. 1207-1212 ◽  
Author(s):  
Jesus Silva ◽  
Noel Varela ◽  
Luz Adriana Borrero López ◽  
Rafael Humberto Rojas Millán
Keyword(s):  
Association Rules ◽  
Customer Segmentation ◽  
Apriori Algorithm ◽  
Rules Extraction

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 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

Mining redundant industrial alarm occurrences with association rules extraction and complex networks modeling

2011 ◽  
Vol 11 (s1) ◽  
pp. S15-S28 ◽  
Author(s):  
Leandro Pfleger de Aguiar ◽  
Virgilio A. F. de Almeida ◽  
Wagner Meira, Jr.
Keyword(s):  
Complex Networks ◽  
Association Rules ◽  
Rules Extraction

scholarly journals Association rules with apriori algorithm and hash-based algorithm

2019 ◽  
Vol 1277 ◽  
pp. 012048
Author(s):  
R D Yulanda ◽  
S Wahyuningsih ◽  
F D T Amijaya
Keyword(s):  
Association Rules ◽  
Apriori Algorithm

scholarly journals Discovering Insightful Rules among Truck Crash Characteristics using Apriori Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-16 ◽  
Author(s):  
Jungyeol Hong ◽  
Reuben Tamakloe ◽  
Dongjoo Park
Keyword(s):  
Risk Factors ◽  
Association Rules ◽  
Weather Conditions ◽  
Parametric Models ◽  
Apriori Algorithm ◽  
Drowsy Driving ◽  
Crash Data ◽  
Rainy Weather ◽  
Crash Characteristics ◽  
Strength Of Association

This study aims to discover hidden patterns and potential relationships in risk factors in freight truck crash data. Existing studies mainly used parametric models to analyze the causes of freight vehicle crashes. However, predetermined assumptions and underlying relationships between independent and dependent variables have been cited as its limitations. To overcome these limitations and provide a better understanding of factors that lead to truck crashes on the expressways, we applied the Association Rules Mining (ARM) technique, which is a nonparametric method. ARM quantifies the interrelationships between the antecedents and consequents of truck-involved crashes and provides researchers with the most influential set of factors that leads to crashes. We utilized a freight vehicle-involved crash data consisting of 19,038 crashes that occurred on the Korean expressways from 2008 to 2017 for this investigation. From the data, 90,951 association rules were generated through ARM employing the Apriori algorithm. The lift values estimated by the Apriori algorithm showed the strength of association between risk factors, and based on the estimated lift values, we identified key crash contributory factors that lead to truck-involved crashes at various segment types, under different weather conditions, considering the driver’s age, crash type, driver’s faults, vehicle size, and roadway geometry type. From the generated rules, we demonstrated that overspeeding with medium-weight trucks was highly associated with crashes during the rainy weather, whereas drowsy driving during the evening was correlated with crashes during fine weather. Segment-related crashes were mainly associated with driver’s faults and roadway geometry. Our results present useful insights and suggestions that can be used by transport stakeholders, including policymakers and researchers, to create relevant policies that will help reduce freight truck crashes on the expressways.

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