Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
The Rough Set Approach to Association Rule Mining
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Maximal Association Rules: A Tool for Mining Associations in Text
Journal of Intelligent Information Systems
A fuzzy soft set theoretic approach to decision making problems
Journal of Computational and Applied Mathematics
Data analysis approaches of soft sets under incomplete information
Knowledge-Based Systems
The normal parameter reduction of soft sets and its algorithm
Computers & Mathematics with Applications
Mining fuzzy association rules from questionnaire data
Knowledge-Based Systems
The parameterization reduction of soft sets and its applications
Computers & Mathematics with Applications
A rough set approach for selecting clustering attribute
Knowledge-Based Systems
Processing online analytics with classification and association rule mining
Knowledge-Based Systems
Approximation reduction in inconsistent incomplete decision tables
Knowledge-Based Systems
SMARViz: Soft Maximal Association Rules Visualization
IVIC '09 Proceedings of the 1st International Visual Informatics Conference on Visual Informatics: Bridging Research and Practice
Data filling approach of soft sets under incomplete information
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part II
A new efficient normal parameter reduction algorithm of soft sets
Computers & Mathematics with Applications
An interval-valued fuzzy soft set approach for normal parameter reduction
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Application of fuzzy soft set in decision making problems based on grey theory
Journal of Computational and Applied Mathematics
A novel soft set approach in selecting clustering attribute
Knowledge-Based Systems
A new view to ring theory via soft union rings, ideals and bi-ideals
Knowledge-Based Systems
Context Based Positive and Negative Spatio-Temporal Association Rule Mining
Knowledge-Based Systems
DFP-Growth: an efficient algorithm for mining frequent patterns in dynamic database
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Scalable technique to discover items support from trie data structure
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
WLAR-Viz: weighted least association rules visualization
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
The Position of Rough Set in Soft Set: A Topological Approach
International Journal of Applied Metaheuristic Computing
Another approach to soft rough sets
Knowledge-Based Systems
EFP-M2: efficient model for mining frequent patterns in transactional database
ICCCI'12 Proceedings of the 4th international conference on Computational Collective Intelligence: technologies and applications - Volume Part II
FSSC: An Algorithm for Classifying Numerical Data Using Fuzzy Soft Set Theory
International Journal of Fuzzy System Applications
International Journal of Software Science and Computational Intelligence
Advances in Fuzzy Systems
Advances in Fuzzy Systems
MAR: Maximum Attribute Relative of soft set for clustering attribute selection
Knowledge-Based Systems
Discovering frequent itemsets on uncertain data: a systematic review
MLDM'13 Proceedings of the 9th international conference on Machine Learning and Data Mining in Pattern Recognition
Usage of Fuzzy, Rough, and Soft Set Approach in Association Rule Mining
International Journal of Artificial Life Research
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In this paper, we present an alternative approach for mining regular association rules and maximal association rules from transactional datasets using soft set theory. This approach is started by a transformation of a transactional dataset into a Boolean-valued information system. Since the ''standard'' soft set deals with such information system, thus a transactional dataset can be represented as a soft set. Using the concept of parameters co-occurrence in a transaction, we define the notion of regular and maximal association rules between two sets of parameters, also their support, confidence and maximal support, maximal confidences, respectively properly using soft set theory. The results show that the soft regular and soft maximal association rules provide identical rules as compared to the regular and maximal association rules.