scholarly journals Synthesizing configuration file specifications with association rule learning

2017 ◽  
Vol 1 (OOPSLA) ◽  
pp. 1-20 ◽  
Author(s):  
Mark Santolucito ◽  
Ennan Zhai ◽  
Rahul Dhodapkar ◽  
Aaron Shim ◽  
Ruzica Piskac
Keyword(s):  
Association Rule ◽  
Rule Learning ◽  
Configuration File

Study of Various Parallel Implementations of Association Rule Mining Algorithm

2020 ◽  
Vol 1 (3) ◽  
pp. 1-7
Author(s):  
Sarbani Dasgupta ◽  
Banani Saha
Keyword(s):  
Association Rule ◽  
Association Rule Mining ◽  
Parallel Implementation ◽  
Rule Learning ◽  
Frequent Itemsets ◽  
Rule Mining ◽  
Large Dataset ◽  
Transactional Databases ◽  
Frequent Itemsets Mining ◽  
Sequential Implementation

In data mining, Apriori technique is generally used for frequent itemsets mining and association rule learning over transactional databases. The frequent itemsets generated by the Apriori technique provides association rules which are used for finding trends in the database. As the size of the database increases, sequential implementation of Apriori technique will take a lot of time and at one point of time the system may crash. To overcome this problem, several algorithms for parallel implementation of Apriori technique have been proposed. This paper gives a comparative study on various parallel implementation of Apriori technique .It also focuses on the advantages of using the Map Reduce technology, the latest technology used in parallelization of large dataset mining.

scholarly journals Consideration of the Effects of Air Temperature on Structural Health Monitoring through Traffic Light-Based Decision-Making Tools

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Philippe Guéguen ◽  
Alexandru Tiganescu
Keyword(s):  
Resonance Frequency ◽  
Association Rule ◽  
Rule Learning ◽  
Operating Conditions ◽  
Economic Losses ◽  
Atmospheric Conditions ◽  
Continuous Assessment ◽  
Traffic Light ◽  
Modal Shape ◽  
Real Time Analysis

The real-time analysis of a structure’s integrity associated with a process to estimate damage levels improves the safety of people and assets and reduces the economic losses associated with interrupted production or operation of the structure. The appearance of damage in a building changes its dynamic response (frequency, damping, and/or modal shape), and one of the most effective methods for the continuous assessment of integrity is based on the use of ambient vibrations. However, although resonance frequency can be used as an indicator of change, misinterpretation is possible since frequency is affected not only by the occurrence of damage but also by certain operating conditions and particularly certain atmospheric conditions. In this study, after analyzing the correlation of resonance frequency values with temperature for one building, we use the data mining method called “association rule learning” (ARL) to predict future frequencies according to temperature measurements. We then propose an anomaly interpretation strategy using the “traffic light” method.

scholarly journals A Darknet Traffic Analysis for IoT Malwares Using Association Rule Learning

2018 ◽  
Vol 144 ◽  
pp. 118-123 ◽  
Author(s):  
Naoki Hashimoto ◽  
Seiichi Ozawa ◽  
Tao Ban ◽  
Junji Nakazato ◽  
Jumpei Shimamura
Keyword(s):  
Association Rule ◽  
Rule Learning ◽  
Traffic Analysis

scholarly journals Association rule learning to improve deficiency inspection in port state control

2019 ◽  
Vol 47 (3) ◽  
pp. 332-351 ◽  
Author(s):  
Wu-Hsun Chung ◽  
Sheng-Long Kao ◽  
Chun-Min Chang ◽  
Chien-Chung Yuan
Keyword(s):  
Association Rule ◽  
Rule Learning ◽  
State Control ◽  
Port State Control

Associative Classification Approaches: Review and Comparison

2014 ◽  
Vol 13 (03) ◽  
pp. 1450027 ◽  
Author(s):  
Neda Abdelhamid ◽  
Fadi Thabtah
Keyword(s):  
Association Rule ◽  
Rule Learning ◽  
Learning Rule ◽  
Data Representation ◽  
Associative Classification ◽  
Data Mining Approach ◽  
Association Rule Discovery ◽  
Multi Class Classification ◽  
Rule Pruning ◽  
New Research

Associative classification (AC) is a promising data mining approach that integrates classification and association rule discovery to build classification models (classifiers). In the last decade, several AC algorithms have been proposed such as Classification based Association (CBA), Classification based on Predicted Association Rule (CPAR), Multi-class Classification using Association Rule (MCAR), Live and Let Live (L3) and others. These algorithms use different procedures for rule learning, rule sorting, rule pruning, classifier building and class allocation for test cases. This paper sheds the light and critically compares common AC algorithms with reference to the abovementioned procedures. Moreover, data representation formats in AC mining are discussed along with potential new research directions.

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