Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences
IEEE Transactions on Knowledge and Data Engineering
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Fast Discovery of Representative Association Rules
RSCTC '98 Proceedings of the First International Conference on Rough Sets and Current Trends in Computing
Geospatial decision support for drought risk management
Communications of the ACM
Clustering of streaming time series is meaningless
DMKD '03 Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery
Sequential Association Rule Mining with Time Lags
Journal of Intelligent Information Systems
Clustering of Time Series Subsequences is Meaningless: Implications for Previous and Future Research
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Building knowledge discovery into a geo-spatial decision support system
Proceedings of the 2003 ACM symposium on Applied computing
Time-series data mining in a geospatial decision support system
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
Periodic association mining in a geospatial decision support system
dg.o '06 Proceedings of the 2006 international conference on Digital government research
Making clustering in delay-vector space meaningful
Knowledge and Information Systems
Unsupervised pattern mining from symbolic temporal data
ACM SIGKDD Explorations Newsletter - Special issue on data mining for health informatics
ACM SIGKDD Explorations Newsletter
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
Mining sequential association rules for traveler context prediction
Proceedings of the 5th Annual International Conference on Mobile and Ubiquitous Systems: Computing, Networking, and Services
Motif discovery in physiological datasets: A methodology for inferring predictive elements
ACM Transactions on Knowledge Discovery from Data (TKDD)
Mining frequent arrangements of temporal intervals
Knowledge and Information Systems
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
RuleGrowth: mining sequential rules common to several sequences by pattern-growth
Proceedings of the 2011 ACM Symposium on Applied Computing
CMRules: Mining sequential rules common to several sequences
Knowledge-Based Systems
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
FTT algorithm of web pageviews for personalized recommendation
ASWC'06 Proceedings of the First Asian conference on The Semantic Web
Deductive and inductive reasoning on spatio-temporal data
INAP'04/WLP'04 Proceedings of the 15th international conference on Applications of Declarative Programming and Knowledge Management, and 18th international conference on Workshop on Logic Programming
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part II
Mining association rules from time series to explain failures in a hot-dip galvanizing steel line
Computers and Industrial Engineering
Closeness Preference - A new interestingness measure for sequential rules mining
Knowledge-Based Systems
TNS: mining top-k non-redundant sequential rules
Proceedings of the 28th Annual ACM Symposium on Applied Computing
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We present MOWCATL, an efficient method for mining frequent sequential association rules from multiple sequential data sets with a time lag between the occurrence of an antecedent sequence and the corresponding consequent sequence. This approach finds patterns in one or more sequences that precede the occurrence of patterns in other sequences, with respect to user-specified constraints. In addition to the traditional frequency and support constraints in sequential data mining, this approach uses separate antecedent and consequent inclusion constraints. Moreover, separate antecedent and consequent maximum window widths are used to specify the antecedent and consequent patterns that are separated by the maximum time lag.We use multiple time series drought risk management data to show that our approach can be effectively employed in real-life problems. The experimental results validate the superior performance of our method for efficiently finding relationships between global climatic episodes and local drought conditions. We also compare our new approach to existing methods and show how they complement each other to discover associations in a drought risk management decision support system.