Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
A model and a language for the fuzzy representation and handling of time
Fuzzy Sets and Systems
Knowledge Discovery from Series of Interval Events
Journal of Intelligent Information Systems - Data warehousing and knowledge discovery
General Temporal Knowledge for Planning and Data Mining
Annals of Mathematics and Artificial Intelligence
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Discovering Temporal Patterns for Interval-Based Events
DaWaK 2000 Proceedings of the Second International Conference on Data Warehousing and Knowledge Discovery
Data Mining for Imprecise Temporal Associations
SNPD-SAWN '05 Proceedings of the Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Networks
Integrating quantitative and qualitative fuzzy temporal constraints
AI Communications - Special issue on: Spatial and temporal reasoning
A temporal constraint structure for extracting temporal information from clinical narrative
Journal of Biomedical Informatics
Temporal data mining for the quality assessment of hemodialysis services
Artificial Intelligence in Medicine
Discovering richer temporal association rules from interval-based data
DaWaK'05 Proceedings of the 7th international conference on Data Warehousing and Knowledge Discovery
Leveraging natural language processing of clinical narratives for phenotype modeling
PIKM '10 Proceedings of the 3rd workshop on Ph.D. students in information and knowledge management
Avian influenza: Temporal modeling of a human to human transmission case
Expert Systems with Applications: An International Journal
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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.