Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
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Data mining: practical machine learning tools and techniques with Java implementations
Sequence mining in categorical domains: incorporating constraints
Proceedings of the ninth international conference on Information and knowledge management
ACM Transactions on Information Systems (TOIS)
Maintaining knowledge about temporal intervals
Communications of the ACM
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Adding Temporal Semantics to Association Rules
PKDD '99 Proceedings of the Third European Conference on Principles of Data Mining and Knowledge Discovery
Mining Associations over Human Sleep Time Series
CBMS '05 Proceedings of the 18th IEEE Symposium on Computer-Based Medical Systems
Mining temporal interval relational rules from temporal data
Journal of Systems and Software
Expert Systems with Applications: An International Journal
Discovering key sequences in time series data for pattern classification
ICDM'06 Proceedings of the 6th Industrial Conference on Data Mining conference on Advances in Data Mining: applications in Medicine, Web Mining, Marketing, Image and Signal Mining
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We introduce an algorithm for mining expressive temporal relationships from complex data. Our algorithm, AprioriSetsAndSequences (ASAS), extends the Apriori algorithm to data sets in which a single data instance may consist of a combination of attribute values that are nominal sequences, time series, sets, and traditional relational values. Data sets of this type occur naturally in many domains including health care, financial analysis, complex system diagnostics, and domains in which multi-sensors are used. AprioriSetsAndSequences identifies predefined events of interest in the sequential data attributes. It then mines for association rules that make explicit all frequent temporal relationships among the occurrences of those events and relationships of those events and other data attributes. Our algorithm inherently handles different levels of time granularity in the same data set. We have implemented AprioriSetsAndSequences within the Weka environment [1] and have applied it to computer performance, stock market, and clinical sleep disorder data. We show that AprioriSetsAndSequences produces rules that express significant temporal relationships that describe patterns of behavior observed in the data set.