文档介绍：Data & Knowledge Engineering 69 (2010) 619–639
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Data & Knowledge Engineering
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TOD: Temporal outlier detection by using quasi-functional
temporal dependencies
Giulia Bruno, Paolo Garza *
Dipartimento di Automatica e Informatica, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
article info abstract
Article history: The problem of detecting outliers has been investigated in several research areas such as
Received 8 May 2009 database, machine learning, knowledge discovery, and logic programming, with the aim
Received in revised form 9 February 2010 of identifying objects of a given population whose behavior is different from that of the
Accepted 11 February 2010
other data objects of the dataset. Outliers represent semantically correct situations, albeit
Available online 1 March 2010
infrequent with respect to the majority of cases. Detecting them allows extracting useful
and actionable knowledge of interest to domain experts. In this paper, we focus our atten-
Keywords:
tion on the analysis of outlier detection in temporal databases. We propose a method,
Knowledge discovery
based on association rules, to infer the normal behavior of objects by extracting frequent
Temporal outlier detection
Temporal databases rules from a given dataset. To include the time information, we define the concept of tem-
Temporal association rules poral association rules. Then, tempo