Mining association rules from imprecise ordinal data
Fuzzy Sets and Systems
A novel approach for discovering retail knowledge with price information from transaction databases
Expert Systems with Applications: An International Journal
Mining fuzzy temporal patterns from process instances with weighted temporal graphs
International Journal of Data Analysis Techniques and Strategies
Fast discovery of sequential patterns in large databases using effective time-indexing
Information Sciences: an International Journal
Handling sequential pattern decay: Developing a two-stage collaborative recommender system
Electronic Commerce Research and Applications
Electronic Commerce Research and Applications
Sequential patterns mining with fuzzy time-intervals
FSKD'09 Proceedings of the 6th international conference on Fuzzy systems and knowledge discovery - Volume 3
A flexible and efficient sequential pattern mining algorithm
International Journal of Intelligent Information and Database Systems
Knowledge gathering of fuzzy multi-time-interval sequential patterns
Information Sciences: an International Journal
Analysis on repeat-buying patterns
Knowledge-Based Systems
Mining weighted sequential patterns in a sequence database with a time-interval weight
Knowledge-Based Systems
Mining fuzzy association rules from uncertain data
Knowledge and Information Systems
Discovering multi-label temporal patterns in sequence databases
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
Generating touring path suggestions using time-interval sequential pattern mining
Expert Systems with Applications: An International Journal
Mining the change of customer behavior in fuzzy time-interval sequential patterns
Applied Soft Computing
Knowledge discovery of weighted RFM sequential patterns from customer sequence databases
Journal of Systems and Software
A data mining approach to discovering reliable sequential patterns
Journal of Systems and Software
Recommendations of closed consensus temporal patterns by group decision making
Knowledge-Based Systems
Mining non-redundant time-gap sequential patterns
Applied Intelligence
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Given a sequence database and minimum support threshold, the task of sequential pattern mining is to discover the complete set of sequential patterns in databases. From the discovered sequential patterns, we can know what items are frequently brought together and in what order they appear. However, they cannot tell us the time gaps between successive items in patterns. Accordingly, Chen et al. have proposed a generalization of sequential patterns, called time-interval sequential patterns, which reveals not only the order of items, but also the time intervals between successive items . An example of time-interval sequential pattern has a form like (A, I2, B, I1, C), meaning that we buy A first, then after an interval of I2 we buy B, and finally after an interval of I1 we buy C, where I2 and I1 are predetermined time ranges. Although this new type of pattern can alleviate the above concern, it causes the sharp boundary problem. That is, when a time interval is near the boundary of two predetermined time ranges, we either ignore or overemphasize it. Therefore, this paper uses the concept of fuzzy sets to extend the original research so that fuzzy time-interval sequential patterns are discovered from databases. Two efficient algorithms, the fuzzy time interval (FTI)-Apriori algorithm and the FTI-PrefixSpan algorithm, are developed for mining fuzzy time-interval sequential patterns. In our simulation results, we find that the second algorithm outperforms the first one, not only in computing time but also in scalability with respect to various parameters.