Geospatial decision support for drought risk management
Communications of the ACM
Discovering Sequential Association Rules with Constraints and Time Lags in Multiple Sequences
ISMIS '02 Proceedings of the 13th International Symposium on Foundations of Intelligent Systems
Sequential Association Rule Mining with Time Lags
Journal of Intelligent Information Systems
Dataless Transitions Between Concise Representations of Frequent Patterns
Journal of Intelligent Information Systems
Building knowledge discovery into a geo-spatial decision support system
Proceedings of the 2003 ACM symposium on Applied computing
Efficient rule discovery in a geo-spatial decision support system
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
A software architecture for distributed geospatial decision support systems
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
Time-series data mining in a geospatial decision support system
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
A knowledge-based geo-spatial decision support system for drought assessment
dg.o '04 Proceedings of the 2004 annual national conference on Digital government research
A software architecture and framework for Web-based distributed Decision Support Systems
Decision Support Systems
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
Efficient mining of frequent episodes from complex sequences
Information Systems
Mining Serial Episode Rules with Time Lags over Multiple Data Streams
DaWaK '08 Proceedings of the 10th international conference on Data Warehousing and Knowledge Discovery
Non-Derivable Item Set and Non-Derivable Literal Set Representations of Patterns Admitting Negation
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
Temporal association rules mining: a heuristic methodology applied to time series databases (TSDBs)
CIMMACS '10 Proceedings of the 9th WSEAS international conference on computational intelligence, man-machine systems and cybernetics
Mining closed episodes with simultaneous events
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast mining of non-derivable episode rules in complex sequences
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Mining association rules in long sequences
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Mining association rules from time series to explain failures in a hot-dip galvanizing steel line
Computers and Industrial Engineering
Discovering forward sequences from temporal data
Knowledge-Based Systems
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Discovering association rules from time-series data is an important data mining problem. The number of potential rules grows quickly as the number of items in the antecedent grows. It is therefore difficult for an expert to analyze the rules and identify the useful. An approach for generating representative association rules for transactions that uses only a subset of the set of frequent itemsets called frequent closed itemsets was presented in [6 ]. We employ formalconcept analysis to develop the notion of frequent closed episodes. The concept of representative association rules is formalized in the context of event sequences. Applying constraints to target highly significant rules further reduces the number of rules. Our approach results in a significant reduction of the number of rules generated, while maintaining the minimum set of relevant association rules and retaining the ability to generate the entire set of association rules with respect to the given constraints. We show how our method can be used to discover associations in a drought risk management decision support system and use multiple climatology datasets related to automated weather stations1