Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
A framework for measuring changes in data characteristics
PODS '99 Proceedings of the eighteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Efficient mining of emerging patterns: discovering trends and differences
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Time series similarity measures (tutorial PM-2)
Tutorial notes of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Time series similarity measures and time series indexing (abstract only)
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Discovering the set of fundamental rule changes
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Fast Time Sequence Indexing for Arbitrary Lp Norms
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
Selecting the right interestingness measure for association patterns
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering Calendar-Based Temporal Association Rules
TIME '01 Proceedings of the Eighth International Symposium on Temporal Representation and Reasoning (TIME'01)
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We study the problem of mining all associated itemsets whose prevalence variations are similar to a given reference sequence from temporal databases. The discovered temporal association patterns can reveal interesting relationships of itemsets which co-occur with a particular event over time. A user-defined subset specification which consists of a reference sequence, a similarity function, and a dissimiliarty threshold is used for defining interesting temporal patterns and guiding the similarity search. We develop algorithms with exploring interesting properties for efficiently finding the similar temporal association patterns. Experimental results show that the proposed algorithms are efficient than a naive approach.