FreeSpan: frequent pattern-projected sequential pattern mining
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Mining hybrid sequential patterns and sequential rules
Information Systems
Extending ERD modeling notation to fuzzy management of GIS data files
Data & Knowledge Engineering
Mining Multiple-Level Association Rules in Large Databases
IEEE Transactions on Knowledge and Data Engineering
Fuzzy association rules and the extended mining algorithms
Information Sciences—Informatics and Computer Science: An International Journal
Enriching web taxonomies through subject categorization of query terms from search engine logs
Decision Support Systems - Web retrieval and mining
Data-Driven Discovery of Quantitative Rules in Relational Databases
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
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Generalized Association Rules
VLDB '95 Proceedings of the 21th 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
HICSS '98 Proceedings of the Thirty-First Annual Hawaii International Conference on System Sciences-Volume 5 - Volume 5
An Approach for Measuring Semantic Similarity between Words Using Multiple Information Sources
IEEE Transactions on Knowledge and Data Engineering
Fuzzy data mining for interesting generalized association rules
Fuzzy Sets and Systems - Theme: Learning and modeling
Mining Sequential Patterns from Multidimensional Sequence Data
IEEE Transactions on Knowledge and Data Engineering
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
A collaborative filtering framework based on fuzzy association rules and multiple-level similarity
Knowledge and Information Systems
Learning fuzzy rules with their implication operators
Data & Knowledge Engineering
A fuzzy Petri net model for intelligent databases
Data & Knowledge Engineering
Fuzzy XML data modeling with the UML and relational data models
Data & Knowledge Engineering
Sequential pattern mining algorithm for automotive warranty data
Computers and Industrial Engineering
On mining multi-time-interval sequential patterns
Data & Knowledge Engineering
Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events
Data & Knowledge Engineering
International Journal of Automation and Computing
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
An effective parallel approach for genetic-fuzzy data mining
Expert Systems with Applications: An International Journal
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Items sold in a store can usually be organized into a concept hierarchy according to a taxonomy. Based on the hierarchy, sequential patterns can be found not only at the leaf nodes (individual items) of the hierarchy, but also at higher levels of the hierarchy; this is called multiple-level sequential pattern mining. In previous research, taxonomies had crisp relationships between the categories in one level and the categories in another level. In real life, however, crisp taxonomies cannot handle the uncertainties and fuzziness inherent in the relationships among items and categories. For example, the book Alice's Adventures in Wonderland can be classified into the Children's Literature category, but can also be related to the Action &Adventure category. To deal with the fuzzy nature of taxonomy, we apply fuzzy set techniques to concept taxonomies so that the relationships from one level to another can be represented by a value between 0 and 1. Accordingly, a fuzzy multiple-level mining algorithm, the fuzzy multi-level sequential mining algorithm (FMSM), is proposed to extract fuzzy multiple-level sequential patterns from databases. In addition, another algorithm, named the CROSS-FMSM algorithm, is developed to discover fuzzy cross-level sequential patterns. Experiments using synthetic datasets show the algorithms' computational efficiency and scalability, and a real dataset is used to prove the patterns' effectiveness.