Genetic Programming for Mining Association Rules in Relational Database Environments

2015 ◽  
pp. 431-450 ◽  
Author(s):  
J. M. Luna ◽  
A. Cano ◽  
S. Ventura
Keyword(s):  
Genetic Programming ◽  
Association Rules ◽  
Relational Database ◽  
Mining Association Rules

Mining association rules on Big Data through MapReduce genetic programming

2017 ◽  
Vol 25 (1) ◽  
pp. 31-48 ◽  
Author(s):  
F. Padillo ◽  
J.M. Luna ◽  
F. Herrera ◽  
S. Ventura
Keyword(s):  
Big Data ◽  
Genetic Programming ◽  
Association Rules ◽  
Mining Association Rules

A General Framework Based on Composite Granules for Mining Association Rules

2014 ◽  
Vol 23 (05) ◽  
pp. 1450009 ◽  
Author(s):  
Gang Fang ◽  
Yue Wu
Keyword(s):  
Information System ◽  
Association Rules ◽  
Relational Database ◽  
General Framework ◽  
Frequent Itemsets ◽  
Data Sets ◽  
Complex Data ◽  
Information Granule ◽  
Mining Association Rules ◽  
Composite Granules

At present many algorithms for mining association rules have been proposed, but most of them are only suitable for discovering specific frequent itemsets from characteristic data sets on the appointed environments, namely, these algorithms are not general enough when mining association rules. In this paper, a general framework based on composite granules for mining association rules is proposed, which is a general data mining model without appointed restriction from frequent itemsets, data sets or mining environments and so on. An iterative method is efficiently applied to the general mining framework for discovering frequent itemsets, which adopts repartitioning frequent attributes to iteratively reconstruct the mixed radix information system for reducing a relational database. In order that the framework for discovering frequent itemsets has a generality, in discussing and establishing the general mining framework, this paper introduces a novel conception and data model, namely, a mixed radix information system is applied to describe a relational database, and a composite granules is used to build a specific relationship between an information system and a mixed radix information system, which can hold the same extension and simultaneously exist in two different information systems. The mixed radix information system can help the general framework to reduce information data and improve the performance of the framework for generating frequent itemsets. The composite granules model can create a relationship between an information granule and a digital information granule, and help the framework for computing the support to avoid reading the database repeatedly or using the complex data structure. Finally, a new taxonomy is presented to verify the generality and the high efficiency of the mining framework and all the experiments based on the taxonomy indicate that the general mining framework has the required generality, and the performance of the framework is better than these classical mining frameworks.

Approach for mining association rules based on rough set

2010 ◽  
Vol 30 (1) ◽  
pp. 25-28 ◽  
Author(s):  
Chao-bo HE ◽  
Qi-mai CHEN
Keyword(s):  
Association Rules ◽  
Rough Set ◽  
Mining Association Rules

Analysis and Evaluation of a Framework for Sampling Database in Recommenders

2018 ◽  
Vol 26 (1) ◽  
pp. 41-57 ◽  
Author(s):  
Hodjat Hamidi ◽  
Reza Mousavi
Keyword(s):  
Association Rules ◽  
Relational Database ◽  
Input Data ◽  
Sampling Method ◽  
Data Integrity ◽  
Sampling Techniques ◽  
System Efficiency ◽  
Production Environment ◽  
Analysis And Evaluation

In this paper the authors proposed a database sampling framework that aims to minimize the time necessary to produce a sample database. They argue that the performance of current relational database sampling techniques that maintain the data integrity of the sample database is low and a faster strategy needs to be devised. The sampling method targets the production environment of a system under development that generally consists of large amounts of data computationally costly to analyze. The results have been improved due to the fact that the authors have selected the users that they had more information about them and they have made the data table denser. Therefore, by increasing the data and making the rating more comprehensive for all the users they can help to produce the more and better association rules. The obtained results were not that much suitable for Jester dataset but with their proposed methods the authors have tried to improve the quantity and quality of the rules. These results indicate that the effectiveness of the system greatly depends on the input data and the applied dataset. In addition, if the user rates more number of the items the system efficiency will be more increased.

Sign in / Sign up

Export Citation Format

Share Document