scholarly journals Association Rule Mining on Big Data Sets

2020 ◽  
Author(s):  
Oguz Celik ◽  
Muruvvet Hasanbasoglu ◽  
Mehmet S. Aktas ◽  
Oya Kalipsiz
Keyword(s):  
Big Data ◽  
Association Rule ◽  
Association Rule Mining ◽  
Data Sets ◽  
Rule Mining

Efficient Association Rule Mining for Retrieving Frequent Itemsets in Big Data Sets

2018 ◽  
Vol 26 (1) ◽  
pp. 1-14
Author(s):  
Chandaka Babi ◽  
Mandapati Rao ◽  
Vedula Rao
Keyword(s):  
Big Data ◽  
Association Rule ◽  
Association Rule Mining ◽  
Frequent Itemsets ◽  
Data Sets ◽  
Rule Mining

scholarly journals Present State-of-The-Art of Association Rule Mining Algorithms

2019 ◽  
Vol 9 (1) ◽  
pp. 6398-6405
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Association Rule Mining ◽  
State Of The Art ◽  
Synthetic Data ◽  
Data Sets ◽  
Evolutionary Analysis ◽  
Rule Mining ◽  
Transaction Database ◽  
Mining Algorithms

A Data mining is the method of extracting useful information from various repositories such as Relational Database, Transaction database, spatial database, Temporal and Time-series database, Data Warehouses, World Wide Web. Various functionalities of Data mining include Characterization and Discrimination, Classification and prediction, Association Rule Mining, Cluster analysis, Evolutionary analysis. Association Rule mining is one of the most important techniques of Data Mining, that aims at extracting interesting relationships within the data. In this paper we study various Association Rule mining algorithms, also compare them by using synthetic data sets, and we provide the results obtained from the experimental analysis

scholarly journals Neutrosophic Association Rule Mining Algorithm for Big Data Analysis

Symmetry ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 106 ◽  
Author(s):  
Mohamed Abdel-Basset ◽  
Mai Mohamed ◽  
Florentin Smarandache ◽  
Victor Chang
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Association Rule ◽  
Association Rule Mining ◽  
Big Data Analysis ◽  
Rule Mining ◽  
Mining Algorithm

APPLYING ASSOCIATION MINING TO CHANGE PROPAGATION

2008 ◽  
Vol 18 (08) ◽  
pp. 1043-1061 ◽  
Author(s):  
LIGUO YU ◽  
STEPHEN R. SCHACH
Keyword(s):  
Association Rule ◽  
Software Maintenance ◽  
Association Rule Mining ◽  
Concept Drift ◽  
Training Data ◽  
Software Systems ◽  
Association Mining ◽  
Change Propagation ◽  
Data Sets ◽  
Rule Mining

A software system evolves as changes are made to accommodate new features and repair defects. Software components are frequently interdependent, so changes made to one component can result in changes having to be made to other components to ensure that the system remains consistent; this is called change propagation. Accurate detection of change propagation is essential for software maintenance, which can be aided by accurate prediction of change propagation. In this paper, we study change propagation in three leading open-source software products: Linux, FreeBSD, and Apache HTTP Server. We use association rules-based data-mining techniques to detect change-propagation rules from the product version history. These rules are evaluated with respect to different training data sets and different test data sets. We discuss the applicability of using association-rule mining for change propagation, and several related issues. We find that a challenging issue in association-rule mining, concept drift, exists in software systems. Concept drift complicates the task of change-propagation prediction and requires special approaches, different from currently-used techniques for predicting change propagation.

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