Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining Techniques: For Marketing, Sales, and Customer Support
Data Mining Techniques: For Marketing, Sales, and Customer Support
Efficient Mining of Association Rules in Distributed Databases
IEEE Transactions on Knowledge and Data Engineering
Mining Open Answers in Questionnaire Data
IEEE Intelligent Systems
Automatic discovery of similarity relationships through Web mining
Decision Support Systems - Web retrieval and mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovery of Multiple-Level Association Rules from Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Fuzzy data mining for interesting generalized association rules
Fuzzy Sets and Systems - Theme: Learning and modeling
Data And Text Mining: A Business Application Approach
Data And Text Mining: A Business Application Approach
Mining massive document collections by the WEBSOM method
Information Sciences: an International Journal - Special issue: Soft computing data mining
Database classification for multi-database mining
Information Systems
Educational data mining: A survey from 1995 to 2005
Expert Systems with Applications: An International Journal
Text document clustering based on frequent word meaning sequences
Data & Knowledge Engineering
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
An efficient algorithm for finding dense regions for mining quantitative association rules
Computers & Mathematics with Applications
Assessing users' product-specific knowledge for personalization in electronic commerce
Expert Systems with Applications: An International Journal
Fuzzy association rules: general model and applications
IEEE Transactions on Fuzzy Systems
Short communication: New results in modelling derived from Bayesian filtering
Knowledge-Based Systems
A soft set approach for association rules mining
Knowledge-Based Systems
Educational data mining: a review of the state of the art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Mining fuzzy specific rare itemsets for education data
Knowledge-Based Systems
An investigation concerning the generation of text summarisation classifiers using secondary data
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Function and service pattern analysis for facilitating the reconfiguration of collaboration systems
Computers and Industrial Engineering
Decision support for improved service effectiveness using domain aware text mining
Knowledge-Based Systems
A semi-automated approach to building text summarisation classifiers
MLDM'12 Proceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition
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Association rule mining is one of most popular data analysis methods that can discover associations within data. Association rule mining algorithms have been applied to various datasets, due to their practical usefulness. Little attention has been paid, however, on how to apply the association mining techniques to analyze questionnaire data. Therefore, this paper first identifies the various data types that may appear in a questionnaire. Then, we introduce the questionnaire data mining problem and define the rule patterns that can be mined from questionnaire data. A unified approach is developed based on fuzzy techniques so that all different data types can be handled in a uniform manner. After that, an algorithm is developed to discover fuzzy association rules from the questionnaire dataset. Finally, we evaluate the performance of the proposed algorithm, and the results indicate that our method is capable of finding interesting association rules that would have never been found by previous mining algorithms.