Mining quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Data mining: concepts and techniques
Data mining: concepts and techniques
Knowledge Discovery in Databases
Knowledge Discovery in Databases
Data Mining: An Overview from a Database Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Discovery of Generalized Association Rules with Multiple Minimum Supports
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Ascending Frequency Ordered Prefix-tree: Efficient Mining of Frequent Patterns
DASFAA '03 Proceedings of the Eighth International Conference on Database Systems for Advanced Applications
Deriving two-stage learning sequences from knowledge in fuzzy sequential pattern mining
Information Sciences—Informatics and Computer Science: An International Journal
Multi-level fuzzy mining with multiple minimum supports
Expert Systems with Applications: An International Journal
SQUIRE: Sequential pattern mining with quantities
Journal of Systems and Software
Mining negative fuzzy sequential patterns
SMO'07 Proceedings of the 7th WSEAS International Conference on Simulation, Modelling and Optimization
A new approach for discovering fuzzy quantitative sequential patterns in sequence databases
Fuzzy Sets and Systems
Mining association rules with multiple minimum supports using maximum constraints
International Journal of Approximate Reasoning
Knowledge gathering of fuzzy multi-time-interval sequential patterns
Information Sciences: an International Journal
From Crispness to Fuzziness: Three Algorithms for Soft Sequential Pattern Mining
IEEE Transactions on Fuzzy Systems
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The objective of mining quantitative sequential patterns is to discover complete sets of sequential patterns with quantities in databases. Although this novel type of pattern considers information, compared to traditional sequential patterns, it contains a sharp boundary problem; that is, when the quantity of an item is near the boundary of two predetermined quantity intervals, it can be either ignored or overemphasized. Hong et al. proposed a new type of pattern, called a fuzzy quantitative sequential pattern (FQSP), to solve this problem. Although this type of pattern provides summary information to expedite decision making, FQSPs confront unrealistic circumstances, that is, they offer only a unique minimum support (min_sup) to all items for the whole database. A higher min_sup fails to find rare items with higher profit, whereas a lower min_sup leads to a combinatorial explosion problem. Instead of this type of mechanism, we used the idea of multiple minimum supports to mine an FQSP. Furthermore, the utility of FQSP is also disturbed because this approach uses only a single membership function without considering the price-quantity relation. The idea of adjustable membership functions was also provided to address this problem. This study proposes a new model to discover an FQSP with both multiple minimum supports and adjustable membership functions. Experiments using synthetic and real datasets demonstrated the computational efficiency, scalability, and effectiveness of the model.