کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
383035 | 660800 | 2013 | 9 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Mining generalized temporal patterns based on fuzzy counting Mining generalized temporal patterns based on fuzzy counting](/preview/png/383035.png)
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 TSETfuzzy-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.
► We propose a fuzzy set-based method for mining generalized event-based temporal patterns.
► The algorithm uses a flexible measure of distance between events.
► The counting method is based on a user-defined fuzzy set associated with the linguistic term approximately equal to.
► The algorithm extracts general, flexible, and practical patterns from real datasets where uncertainty and variability are presented.
► We have carried out a series of experiments to assess and characterize the algorithm with both, synthetic and real datasets.
Journal: Expert Systems with Applications - Volume 40, Issue 4, March 2013, Pages 1296–1304