DOI:​10.1016/j.ins.2013.01.028
Corpus ID: 16838767
Mining numerical association rules via multi-objective genetic algorithms
B. Minaei-Bidgoli, R. Barmaki, M. Nasiri
Published 2013
Mathematics, Computer Science

Inf. Sci.
Association rule discovery is an ever increasing area of interest in data mining. Finding rules for attributes with numerical values is still a challenging point in the process of association rule… Expand
View Via Publisher
Researchgate.Net
Share This Paper




91 Citations
Highly Influential Citations
8
Background Citations
40
Methods Citations
30
Results Citations
3
Figures, Tables, and Topics from this paper
Figure 1
Figure 2
Table 2
Figure 3
Table 3
Figure 4
Table 4
Table 5
Table 6
Table 7
Genetic algorithm
Association rule learning
Numerical analysis
Data mining
Rough set
Pareto efficiency
Level of measurement
Multi-objective optimization
Mathematical optimization
Crossover (genetic algorithm)
Discretization
Database
Mutation (genetic algorithm)
91 Citations
A novel hybrid GA–PSO framework for mining quantitative association rules
F. Moslehi, Abdorrahman Haeri, F. Martínez-Álvarez
Computer Science
Soft Comput.

2020
TLDR
A hybrid algorithm for extracting quantitative association rules from continuous numeric datasets by hybridization of the GA and the PSO algorithm has achieved considerable improvements compared with the basic algorithms in the criteria of the number of extracted rules from dataset, high confidence measure and support percentage. Expand
15 Citations
Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
Vahid Beiranvand, Mohamad Mobasher-Kashani, A. Bakar
Mathematics, Computer Science
Expert Syst. Appl.
2014
TLDR
The results show that MOPAR extracts reliable (with confidence values close to 95%), comprehensible, and interesting numerical ARs when attaining the optimal trade-off between confidence, comprehensibility and interestingness. Expand
64 Citations
Wolf search algorithm for numeric association rule mining
I. E. Agbehadji, S. Fong, R. Millham
Computer Science
2016 IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBDA)
2016
TLDR
A new meta-heuristic algorithm which uses wolf search algorithm (WSA) for numeric association rule mining from rough values within tolerable ranges is proposed. Expand
18 Citations
Improving Association Rules by Optimizing Discretization Based on a Hybrid GA: A Case Study of Data from Forest Ecology Stations in China
Jianxin Wang, Fan Yang, Xiaoli Dong, Ben Xu, Baojiang Cui
Computer Science
2013 Fourth International Conference on Emerging Intelligent Data and Web Technologies
2013
TLDR
This work searches the optimal combination of dividing points from continuous intervals by employing genetic algorithms (GA), in which the properties of strong association rules correspondingly yielded are treated as fitness function to guide the algorithm iteration. Expand
2 Citations
Association rule mining using hybrid GA-PSO for multi-objective optimisation
Aashna Agarwal, Nirali R. Nanavati
Computer Science
2016 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC)
2016
TLDR
The primary advantage of the proposed algorithm is that the hybridisation of multiple objective-GA with multi objective-PSO balances the exploration and exploitation tasks, resulting in valuable extraction of accurate and interpretable mined rules.Expand
23 Citations
A genetic algorithm-based framework for mining quantitative association rules without specifying minimum support and minimum confidence
F. Moslehi, Abdorrahman Haeri
Computer Science
2019
TLDR
A multi-objective algorithm for mining quantitative association rules is proposed based on the Genetic Algorithm, and there is no need to determine the extent of the threshold for the support and confidence criteria.Expand
Genetic algorithm with a structure-based representation for genetic-fuzzy data mining
Chuan-Kang Ting, Ting-Chen Wang, Rung-Tzuo Liaw, T. Hong
Mathematics, Computer ScienceSoft Comput.
2017
TLDR
This study designs a novel chromosome representation considering the structures of membership functions and presents two heuristics in the light of overlap and coverage for removing inappropriate arrangement that achieve significant improvement on GA in terms of solution quality and convergence speed. Expand
11 Citations
PDF
The Rules Determination of Numerical Association Rule Mining Optimization by Using Combination of PSO and Cauchy Distribution
Imam Tahyudin, Hidetaka Nambo
Mathematics
2017
One of the optimization methods to solve the numerical association rule mining problem is particle swarm optimization (PSO). This method is popularly used in various fields such as in the job… Expand
2 Citations
Multi-objective Bat Algorithm for Mining Interesting Association Rules
K. Heraguemi, Nadjet Kamel, H. Drias
Computer Science
MIKE

2016
TLDR
The outcomes show a clear superiority of the proposal in-face-of mono objective methods in terms generated rules number and rule quality against multi-objective optimization methods. Expand
2 Citations
An Optimized Association Rule Mining using Genetic Algorithm
Dimple S. Kanani, Shailendra Mishra
Computer Science
2015
TLDR
The key focus of this synthesize approach is to optimize the rule that generated by mining methodology and to provide more accurate results.Expand
39 Citations
...
1
2
3
4
...
References
SHOWING 1-10 OF 47 REFERENCES
Extraction of interesting association rules using genetic algorithms
Peter P. Wakabi-Waiswa, V. Baryamureeba
Computer Science
2007
TLDR
This paper presents a Pareto−based multi−objective evolutionary algorithm rule mining method based on genetic algorithms that uses confidence, comprehensibility, interestingness, surprise as objectives of the association rule mining problem.Expand
65 Citations
Multi-Objective Rule Mining Using Simulated Annealing Algorithm
Mehdi Nasiri, L. Taghavi, B. Minaee
Computer ScienceJ. Convergence Inf. Technol.
2010
TLDR
A Simulated Annealing algorithm is used to extract some useful and interesting rules from any type databases and the experimental results show that the algorithm may be suitable for large datasets. Expand
21 Citations
PDF
Multi-objective rule mining using genetic algorithms
Ashish Ghosh, B. Nath
Mathematics, Computer ScienceInf. Sci.
2004
TLDR
This article uses a Pareto based genetic algorithm to extract some useful and interesting rules from any market-basket type database and has been found suitable for large databases.Expand
230 Citations
PDF
MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules
Bilal Alatas, E. Akin, A. Karci
Mathematics, Computer Science
Appl. Soft Comput.
2008
In this paper, a Pareto-based multi-objective differential evolution (DE) algorithm is proposed as a search strategy for mining accurate and comprehensible numeric association rules (ARs) which are… Expand
182 Citations
Genetic algorithm based framework for mining fuzzy association rules
M. Kaya, R. Alhajj
Mathematics, Computer ScienceFuzzy Sets Syst.
2005
TLDR
An automated method for mining fuzzy association rules using a genetic algorithm (GA) based clustering method that adjusts centroids of the clusters, which are to be handled later as midpoints of triangular membership functions. Expand
132 Citations
PDF
Multi objective association rule mining with genetic algorithm without specifying minimum support and minimum confidence
Hamid Reza Qodmanan, M. Nasiri, B. Minaei-Bidgoli
Computer ScienceExpert Syst. Appl.
2011
TLDR
This paper proposes a method based on genetic algorithm without taking the minimum support and confidence into account, and applies the FP-tree algorithm in order to improve algorithm efficiency. Expand
132 Citations
Discovering Numeric Association Rules via Evolutionary Algorithm
J. Vázquez, José Luis Álvarez Macías, José Cristóbal Riquelme Santos
Computer Science
PAKDD

2002
TLDR
This paper uses an evolutionary algorithm to find the intervals of each attribute that conforms a frequent itemset within the database and evaluates the tool with synthetic and real databases to check the efficiency of the algorithm. Expand
54 Citations
PDF
An efficient clustering algorithm for mining fuzzy quantitative association rules
Been-Chian Chien, Zin-Long Lin, T. Hong
Mathematics
Proceedings Joint 9th IFSA World Congress and 20th NAFIPS International Conference (Cat. No. 01TH8569)
2001
Mining association rules on categorical data has been discussed widely. It is a relatively difficult problem in the discovery of association rules from numerical data, since the reasonable intervals… Expand
33 Citations
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Dr. Alex A. Freitas
Computer ScienceNatural Computing Series
2002
TLDR
This book integrates two areas of computer science, namely data mining and evolutionary algorithms, and emphasizes the importance of discovering comprehensible, interesting knowledge, which is potentially useful for intelligent decision making. Expand
748 Citations
PDF
Deriving support threshold values and membership functions using the multiple-level cluster-based master–slave IFG approach
Mojtaba Asadollahpour Chamazi, B. Minaei-Bidgoli, M. Nasiri
Mathematics, Computer ScienceSoft Comput.
2013
TLDR
A comprehensive and fast algorithm that mines level-crossing fuzzy association rules on multiple concept levels with learning support threshold values and membership functions using the cluster-based master–slave integrated FG approach.Expand
6 Citations
...
1
2
3
4
...
SORT BY
Related Papers
Wolf search algorithm for numeric association rule mining
I. E. Agbehadji, S. Fong, R. Millham
Computer Science
IEEE International Conference on Cloud Computing…
2016
Big data has become one of the key sources for valuable information and as information becomes larger it poses some comp...
18 Citations
New Directions in Rough Sets, Data Mining, and Granular-Soft Computing
N. Zhong, A. Skowron, S. Ohsuga
Mathematics, Computer Science
Lecture Notes in Computer Science
1999
Invited Talks.- Decision Rules, Bayes' Rule and Rough Sets.- A New Direction in System Analysis: From Computation with M...
279 Citations
Show More
2/10
Abstract
Figures, Tables, and Topics
91 Citations
47 References
Related Papers
Stay Connected With Semantic Scholar
What Is Semantic Scholar?
Semantic Scholar is a free, AI-powered research tool for scientific literature, based at the Allen Institute for AI.
Learn More
About
About Us
Publishers
Beta Program
Contact
Research
Team
Datasets
Open Corpus
Supp.ai
Resources
Librarians
Tutorials
FAQ
API
Proudly built by AI2
Terms of ServicePrivacy Policy
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy, Terms of Service, and Dataset License
ACCEPT & CONTINUE