Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Fast subsequence matching in time-series databases
SIGMOD '94 Proceedings of the 1994 ACM SIGMOD international conference on Management of data
Journal of the American Society for Information Science - Special issue: knowledge discovery and data mining
Fundamentals of Database Systems
Fundamentals of Database Systems
Efficient Similarity Search In Sequence Databases
FODO '93 Proceedings of the 4th International Conference on Foundations of Data Organization and Algorithms
On the need for time series data mining benchmarks: a survey and empirical demonstration
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Redundant association rules reduction techniques
International Journal of Business Intelligence and Data Mining
The importance of negative associations and the discovery of association rule pairs
International Journal of Business Intelligence and Data Mining
Cluster analysis on time series gene expression data
International Journal of Business Intelligence and Data Mining
Correlation maximisation-based discretisation for supervised classification
International Journal of Business Intelligence and Data Mining
A new parallel association rule mining algorithm on distributed shared memory system
International Journal of Business Intelligence and Data Mining
FAR-miner: a fast and efficient algorithm for fuzzy association rule mining
International Journal of Business Intelligence and Data Mining
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Association rule mining is one of the most popular data-mining techniques used to find associations existing between a set of objects or data. A time series is a sequence of observations stamped over the time; Time-series analysis has been used in a variety of applications like: business and health. The application of association mining to time series is very promising. The purpose of this article is to propose a new fast algorithm to discover the association that can exist between two time series. We use discretisation to segment time series to a number of shapes, and we classify these shapes to pre-defined shape classes to generate association rules using Genetic Algorithm (GA).