Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Pruning and summarizing the discovered associations
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
Alternative Interest Measures for Mining Associations in Databases
IEEE Transactions on Knowledge and Data Engineering
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
MSWiM '04 Proceedings of the 7th ACM international symposium on Modeling, analysis and simulation of wireless and mobile systems
Vehicle classification in distributed sensor networks
Journal of Parallel and Distributed Computing
estWin: Online data stream mining of recent frequent itemsets by sliding window method
Journal of Information Science
DSTree: A Tree Structure for the Mining of Frequent Sets from Data Streams
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
CanTree: a canonical-order tree for incremental frequent-pattern mining
Knowledge and Information Systems
PRESTO: a predictive storage architecture for sensor networks
HOTOS'05 Proceedings of the 10th conference on Hot Topics in Operating Systems - Volume 10
Frequent pattern mining: current status and future directions
Data Mining and Knowledge Discovery
A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Mining frequent itemsets over data streams using efficient window sliding techniques
Expert Systems with Applications: An International Journal
Efficient single-pass frequent pattern mining using a prefix-tree
Information Sciences: an International Journal
Sliding window-based frequent pattern mining over data streams
Information Sciences: an International Journal
Mining both associated and correlated patterns
ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part IV
Online algorithms for mining inter-stream associations from large sensor networks
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
IEEE Communications Magazine
Middleware to support sensor network applications
IEEE Network: The Magazine of Global Internetworking
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WSNs generate a large amount of data in the form of data stream; and mining these streams to extract useful knowledge is a highly challenging task. Existing works proposed in literature use sensor association rules measured in terms of occurrence frequency of patterns. However, these rules often generate a huge number of rules, most of which are non-informative or fail to reflect the true correlation among data objects. Additionally mining associated sensor patterns from sensor stream data, which is vital for real-time applications, has not been addressed yet in literature. In this paper, we address these problems and propose a new type of sensor behavioral pattern called associated sensor patterns which capture simultaneously association-like co-occurrence as well as substantial temporal correlations implied by such co-occurrences in sensor data. We propose a novel tree structure, called associated sensor pattern stream tree (ASPS-tree) and a new technique, called associated sensor pattern mining of data stream (ASPMS), using sliding window-based associated sensor pattern mining for WSNs. By capturing the useful knowledge of the data stream into an ASPS-tree, our ASPMS algorithm can mine associated sensor patterns in the current window with frequent pattern (FP)-growth like pattern-growth method. Extensive experimental analyses show that our technique is very efficient in discovering associated sensor patterns over sensor data stream.