Efficient parallel data mining for association rules
CIKM '95 Proceedings of the fourth international conference on Information and knowledge management
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Efficient enumeration of frequent sequences
Proceedings of the seventh international conference on Information and knowledge management
Continuous categories for a mobile robot
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A review on time series data mining
Engineering Applications of Artificial Intelligence
Exploiting efficient parallelism for mining rules in time series data
HPCC'05 Proceedings of the First international conference on High Performance Computing and Communications
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A tremendous growing interest in finding dependency among patterns has been developing in the domain of time series data mining. It is quite effective to find how current and past values in the streams of data are related to the future. However, these kind of data sets with high dimensionality are enormous in size results in possibly large number of mined dependencies. This strongly motivates the need of efficient parallel algorithms. In this paper, we propose two parallel algorithms to discover dependency from the large amount of time series data. We introduce the method of extracting sequence of symbols from the time series data by using segmentation and clustering processes. To reduce the search space and speed up the process we investigate the technique to group the time series data. The experimental results conducted on a shared memory multiprocessors system justifies the inevitability of using parallel techniques for mining huge amount of data in the time series domain.