- Title
- Regularly frequent patterns mining from sensor data stream
- Creator
- Rashid, Md. Mamunur; Gondal, Iqbal; Kamruzzaman, Joarder
- Date
- 2013
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/74354
- Identifier
- vital:7253
- Identifier
- ISBN:9783642420412
- Identifier
-
https://doi.org/10.1007/978-3-642-42042-9_52
- Abstract
- Mining interesting and useful knowledge from the huge amount of data gathered in wireless sensor networks is a challenging task. Works reported in literature use support metric-based sensor association rule which employs the occurrence frequency of patterns as criteria. Such criteria may not be appropriate for finding significant patterns. Moreover, temporal regularity in occurrence behavior should be considered as another important measure for assessing the importance of patterns in WSNs. Frequent sensor patterns that occur after regular intervals is called regularly frequent sensor patterns. Even though mining regularly frequent sensor patterns from sensor data stream is extremely important in many real-time applications, no such algorithm has been proposed yet. In this paper, we propose a novel tree structure called Regularly Frequent Sensor Pattern-tree (RSP-tree) and an efficient mining approach for finding regularly frequent sensor patterns from WSNs. Extensive performance analyses show that our technique is time and memory efficient in finding regularly frequent sensor patterns.
- Publisher
- Springer-Verlag
- Relation
- International Conference on Neural Information Processing (ICONIP 2013) p. 417-424
- Rights
- This metadata is freely available under a CCO license
- Subject
- 0801 Artificial Intelligence and Image Processing; 0805 Distributed Computing; Wireless sensor networks; Data mining; Knowledge discovery; Frequent pattern; Regularly frequent sensor pattern
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