Weighted fuzzy pattern matching
Fuzzy Sets and Systems - Mathematical Modelling
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast discovery of association rules
Advances in 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
Graphical Models: Methods for Data Analysis and Mining
Graphical Models: Methods for Data Analysis and Mining
Association Rules & Evolution in Time
SETN '02 Proceedings of the Second Hellenic Conference on AI: Methods and Applications of Artificial Intelligence
Mining Surprising Patterns Using Temporal Description Length
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
Keeping things simple: finding frequent item sets by recursive elimination
Proceedings of the 1st international workshop on open source data mining: frequent pattern mining implementations
Ranking discovered rules from data mining with multiple criteria by data envelopment analysis
Expert Systems with Applications: An International Journal
Elicitation of fuzzy association rules from positive and negative examples
Fuzzy Sets and Systems
A note on quality measures for fuzzy association rules
IFSA'03 Proceedings of the 10th international fuzzy systems association World Congress conference on Fuzzy sets and systems
Discovering gene association networks by multi-objective evolutionary quantitative association rules
Journal of Computer and System Sciences
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We propose a user-centric rule filtering method that allows to identify association rules that exhibit a certain user-specified temporal behavior with respect to rule evaluation measures. The method can considerably reduce the number of association rules that have to be assessed manually after a rule induction. This is especially necessary if the rule set contains many rules as it is the case for the task of finding rare patterns inside the data. For the proposed method, we will reuse former work on the visualization of association rules [M. Steinbrecher, R. Kruse, Visualization of possibilistic potentials, in: Foundations of Fuzzy Logic and Soft Computing, in: Lecture Notes in Comput. Sci., vol. 4529, Springer-Verlag, Berlin/Heidelberg, 2007, pp. 295-303] and use an extension of it to motivate and assess the presented filtering technique. We put the focus on rules that are induced from a data set that contains a temporal variable and build our approach on the requirement that temporally ordered sets of association rules are available, i.e., one set for every time frame. To illustrate this, we propose an ad-hoc learning method along the way. The actual rule filtering is accomplished by means of fuzzy concepts. These concepts use linguistic variables to partition rule-related domains of interest, such as the confidence change rate. The original rule sets are then matched against these user concepts and result in only those rules that match the respective concepts to a predefined extent. We provide empirical evidence by applying the proposed methods to hand-crafted as well as real-world data sets and critically discuss the current state and further prospects.