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
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Wireless sensor networks: a survey
Computer Networks: The International Journal of Computer and Telecommunications Networking
Directed diffusion for wireless sensor networking
IEEE/ACM Transactions on Networking (TON)
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Ad Hoc Wireless Networks: Architectures and Protocols
Ad Hoc Wireless Networks: Architectures and Protocols
Wireless Sensor Networks: An Information Processing Approach
Wireless Sensor Networks: An Information Processing Approach
A Novel Algorithm for Mining Association Rules in Wireless Ad Hoc Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
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
ICCCI'10 Proceedings of the Second international conference on Computational collective intelligence: technologies and applications - Volume Part III
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Wireless Sensor Networks (WSNs) produce large scale of data in the form of streams. Recently, data mining techniques have received a great deal of attention in extracting knowledge from WSNs data. Mining association rules on the sensor data provides useful information for different applications. Even though there have been some efforts to address this issue in WSNs, they are not suitable when multiple database scans are the major limitation. In this paper, we propose a new tree-based data structure called Sensor Pattern Tree (SP-tree) to generate association rules from WSNs data with one database scan. The SP-tree is constructed in frequency-descending order, which facilitates an efficient mining using the FP-growth-based [6] mining technique. The experimental results show that SP-tree outperforms related algorithms in generating association rules from WSNs data.