.png){kind=icon}
.png){kind=icon}
Return
Mining generalized temporal patterns based on fuzzy counting
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2012.08.061.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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. (C) 2012 Elsevier Ltd. All rights reserved.
Keywords:
Temporal data mining
Fuzzy sets
Temporal patterns
Event-based sequences
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.5
Papers:
2.9W
Citations:
10.2W
View more organizations