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
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Parallel Mining of Association Rules
IEEE Transactions on Knowledge and Data Engineering
A Survey of Temporal Knowledge Discovery Paradigms and Methods
IEEE Transactions on Knowledge and Data Engineering
Mining Sequential Patterns: Generalizations and Performance Improvements
EDBT '96 Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Combining Nearest Neighbor Classifiers Through Multiple Feature Subsets
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Temporal Rule Discovery for Time-Series Satellite Images and Integration with RDB
PKDD '01 Proceedings of the 5th European Conference on Principles of Data Mining and Knowledge Discovery
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering of Correlation from Multi-stream of Human Motion
DS '00 Proceedings of the Third International Conference on Discovery Science
Parallel Sequence Mining on Shared-Memory Machines
Revised Papers from Large-Scale Parallel Data Mining, Workshop on Large-Scale Parallel KDD Systems, SIGKDD
StatStream: statistical monitoring of thousands of data streams in real time
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
Parallel algorithms for mining association rules in time series data
ISPA'03 Proceedings of the 2003 international conference on Parallel and distributed processing and applications
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Mining interesting rules from time series data has earned a lot of attention to the data mining community recently. It is quite useful to extract important patterns from time series data to understand how the current and the past values of patterns in the multivariate time series data are related to the future. These relations can basically be expressed as rules. Mining these interesting rules among patterns is time consuming and expensive in multi-stream data. Incorporating parallel processing techniques is helpful to solve the problem. In this paper, we present a parallel algorithm based on a lattice theoretic approach to find out the rules among patterns that sustain sequential nature in the multi-stream data of time series. The human motion data considered as multi-stream multidimensional data used as data set for this purpose is transformed into sequences of symbols of lower dimension due to its complex nature. Then the proposed algorithm is implemented on a Distributed Shared Memory (DSM) multiprocessors system. The experimental results justify the efficiency of finding rules from the sequences of the patterns for time series data by achieving significant speed up comparing with the previous reported algorithm.