Paper Info
Title
Temporal Data Mining with Temporal Constraints
Abstract
Nowadays, methods for discovering temporal knowledge try to extract more complete and representative patterns. The use of qualitative temporal constraints can be helpful in that aim, but its use should also involve methods for reasoning with them (instead of using them just as a high level representation) when a pattern consists of a constraint network instead of an isolated constraint.In this paper, we put forward a method for mining temporal patterns that makes use of a formal model for representing and reasoning with qualitative temporal constraints. Three steps should be accomplished in the method: 1) the selection of a model that allows a trade off between efficiency and representation; 2) a preprocessing step for adapting the input to the model; 3) a data mining algorithm able to deal with the properties provided by the model for generating a representative output.In order to implement this method we propose the use of the Fuzzy Temporal Constraint Network (FTCN) formalism and of a temporal abstraction method for preprocessing. Finally, the ideas of the classic methods for data mining inspire an algorithm that can generate FTCNs as output.Along this paper, we focus our attention on the data mining algorithm.
Year
DOI
Venue
2007
10.1007/978-3-540-73599-1_8
AIME '87
Keywords
Field
DocType
constraint network,data mining,temporal abstraction method,classic method,high level representation,temporal constraints,formal model,temporal data mining,data mining algorithm,temporal pattern,temporal knowledge,qualitative temporal constraint
Data mining,Local consistency,Abstraction,Computer science,Fuzzy logic,Preprocessor,Artificial intelligence,Formalism (philosophy),Data mining algorithm,Temporal data mining,Machine learning
Conference
Volume
ISSN
Citations 
4594
0302-9743
8
PageRank 
References 
Authors
0.55
12
3
Name
Order
Citations
PageRank
M. Campos1121.98
José Palma212014.28
R. Marín3716.23