The temporal query language TQuel
ACM Transactions on Database Systems (TODS)
A homogeneous relational model and query languages for temporal databases
ACM Transactions on Database Systems (TODS)
A temporal relational model and a query language
Information Sciences: an International Journal
Fundamentals of database systems (2nd ed.)
Fundamentals of database systems (2nd ed.)
Temporal database modeling: an object-oriented approach
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Maintaining knowledge about temporal intervals
Communications of the ACM
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Visual Query Operators for Temporal Databases
TIME '97 Proceedings of the 4th International Workshop on Temporal Representation and Reasoning (TIME '97)
Behavior Discovery as Database Scheme Design
TIME '00 Proceedings of the Seventh International Workshop on Temporal Representation and Reasoning (TIME'00)
Clustering stream data by regression analysis
ACSW Frontiers '04 Proceedings of the second workshop on Australasian information security, Data Mining and Web Intelligence, and Software Internationalisation - Volume 32
Information Preserving Time Decompositions of Time Stamped Documents*
Data Mining and Knowledge Discovery
A review on time series data mining
Engineering Applications of Artificial Intelligence
Disjunctive sequential patterns on single data sequence and its anti-monotonicity
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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In this investigation, we discuss how to mine Temporal Class Schemes to model a collection of time series data. From the viewpoint of temporal data mining, this problem can be seen as discretizing time series data or aggregating them. Also this can be considered as screening (or noise filtering). From the viewpoint of temporal databases, the issue is how we represent the data and how we can obtain intensional aspects as temporal schemes. In other words, we discuss scheme discovery for temporal data. Given a collection of temporal objects along with time axis (called log), we examine the data and we introduce a notion of temporal frequent classes to describe them. As the main results of this investigation, we can show that there exists one and only one interval decomposition and the temporal classes related to them. Also we give experimental results that prove the feasibility to time series data.