Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Variable precision rough set model
Journal of Computer and System Sciences
Rough set approach to incomplete information systems
Information Sciences: an International Journal
Rules in incomplete information systems
Information Sciences: an International Journal
Unsupervised Rough Set Classification Using GAs
Journal of Intelligent Information Systems
Data mining for design and manufacturing
Formalizing Calendars with the Category of Ordinals
Applied Intelligence
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Rough set methods in feature selection and recognition
Pattern Recognition Letters - Special issue: Rough sets, pattern recognition and data mining
Data Analysis and Mining in Ordered Information Tables
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Rough Sets and Knowledge Discovery: An Overview
RSKD '93 Proceedings of the International Workshop on Rough Sets and Knowledge Discovery: Rough Sets, Fuzzy Sets and Knowledge Discovery
Approaches to knowledge reduction based on variable precision rough set model
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Applied Intelligence
Applied Intelligence
Rough sets and ordinal reducts
Soft Computing - A Fusion of Foundations, Methodologies and Applications
MMR: An algorithm for clustering categorical data using Rough Set Theory
Data & Knowledge Engineering
Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
Consistent models of transitivity for reciprocal preferences on a finite ordinal scale
Information Sciences: an International Journal
Discovering patterns of missing data in survey databases: An application of rough sets
Expert Systems with Applications: An International Journal
Ontology-based data mining approach implemented on exploring product and brand spectrum
Expert Systems with Applications: An International Journal
Implement web learning environment based on data mining
Knowledge-Based Systems
Spatially enabled customer segmentation using a data classification method with uncertain predicates
Decision Support Systems
A Dominance-based Rough Set Approach to customer behavior in the airline market
Information Sciences: an International Journal
Qualitative probabilistic networks with reduced ambiguities
Applied Intelligence
Rough sets for adapting wavelet neural networks as a new classifier system
Applied Intelligence
Expert Systems with Applications: An International Journal
Data stream classification with artificial endocrine system
Applied Intelligence
Mining interesting user behavior patterns in mobile commerce environments
Applied Intelligence
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When items are classified according to whether they have more or less of a characteristic, the scale used is referred to as an ordinal scale. The main characteristic of the ordinal scale is that the categories have a logical or ordered relationship to each other. Thus, the ordinal scale data processing is very common in marketing, satisfaction and attitudinal research. This study proposes a new data mining method, using a rough set-based association rule, to analyze ordinal scale data, which has the ability to handle uncertainty in the data classification/sorting process. The induction of rough-set rules is presented as method of dealing with data uncertainty, while creating predictive if--then rules that generalize data values, for the beverage market in Taiwan. Empirical evaluation reveals that the proposed Rough Set Associational Rule (RSAR), combined with rough set theory, is superior to existing methods of data classification and can more effectively address the problems associated with ordinal scale data, for exploration of a beverage product spectrum.