Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
The three semantics of fuzzy sets
Fuzzy Sets and Systems - Special issue: fuzzy sets: where do we stand? Where do we go?
ACM Transactions on Information Systems (TOIS)
SPADE: an efficient algorithm for mining frequent sequences
Machine Learning
Discovery of Frequent Episodes in Event Sequences
Data Mining and Knowledge Discovery
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Discovering calendar-based temporal association rules
Data & Knowledge Engineering - Special issue: Temporal representation and reasoning
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
A template model for multidimensional inter-transactional association rules
The VLDB Journal — The International Journal on Very Large Data Bases
An efficient algorithm for mining frequent inter-transaction patterns
Information Sciences: an International Journal
Information Sciences: an International Journal
Mining weighted sequential patterns in a sequence database with a time-interval weight
Knowledge-Based Systems
Using a projection-based approach to mine frequent inter-transaction patterns
Expert Systems with Applications: An International Journal
An empirical study on mining sequential patterns in a grid computing environment
Expert Systems with Applications: An International Journal
Temporal representation and reasoning in artificial intelligence: A review
Mathematical and Computer Modelling: An International Journal
A tree structure for event-based sequence mining
Knowledge-Based Systems
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Event-based sequences are a kind of pattern based on temporal associations with two essential characteristics: they are syntactically simple and have a great expressive power. For this reason, event-based sequence mining is an interesting solution to the problem of knowledge discovery in dynamic domains, mainly characterized by a time-varying nature. The inter-transactional model has led to the design of algorithms aimed to obtain this sort of patterns from time-stamped datasets. These algorithms extend the well-known Apriori algorithm, by explicitly adding the temporal context where associations among frequent events occurs. This leads to the possibility of extracting a larger number of patterns with a potential interest in decision making. However, its usefulness is diminished in those datasets where the characteristics of variability and uncertainty are present, which is a common issue in real domains. This is due to the rigidity of the counting method, which uses an exact measure of distance between temporal events. As a solution, we propose a generalization of the temporal mining process, which implies a relaxation of the counting method including the concept of approximate temporal distance between events. In particular, in this paper we present an algorithm, called TSET^f^u^z^z^y-Miner, which incorporates a fuzzy-based counting technique in order to extract general, flexible, and practical temporal patterns taking into account the particular characteristics of real domains.