Outlier detection is a well studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a p...
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Outlier detection is a well studied problem in various fields. The unique characteristics and constraints of wireless sensor networks (WSN) make this problem especially challenging. Sensors can detect outliers for a plethora of reasons and these reasons need to be inferred in real time. Here, we survey the current state of research in this area, compare them and present some future directions for smarter handling of outliers in WSN.
In this research work a new hybrid approach to spatio-temporal seismic clustering is proposed. The method builds upon a novel densitybased clustering scheme that explicitly takes into account earthquake's magnitu...
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In this research work a new hybrid approach to spatio-temporal seismic clustering is proposed. The method builds upon a novel densitybased clustering scheme that explicitly takes into account earthquake's magnitude during the density estimation. The new densitybased clustering algorithm considers both time and spatial information during cluster formation. Therefore clusters lie in a spatio-temporal space. A hierarchical agglomerative clustering algorithm acts upon the identified clusters after dropping the time information in order to come up only with the spatial description of seismic events. The approach is demonstrated using data from the vicinity of the Hellenic seismic arc in order to enable its comparison with some of the state-of-the-art distinct seismic region identification methodologies. The presented results indicate that the combination of the two clustering stages could be potentially used for an automatic definition of major seismic sources. (C) 2013 Elsevier Ltd. All rights reserved.
Mining association rules plays an essential role in data mining tasks. Many algorithms have been proposed for mining Boolean association rules, but they cannot deal with quantitative and categorical data directly. Alt...
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ISBN:
(纸本)9781424421077
Mining association rules plays an essential role in data mining tasks. Many algorithms have been proposed for mining Boolean association rules, but they cannot deal with quantitative and categorical data directly. Although we can transform quantitative attributes into intervals and applying Boolean algorithms to the intervals. But this approach is not effective and is difficult to scale tip for high-dimensional cases. An efficient algorithm, DBSMiner (densitybased Sub-space Miner), is proposed by using the notion of "density- connected" to cluster the high density sub-space of quantitative attributes and gravitation between grid / cluster to deal with the low density cells which may be missed by the previous algorithms, DBSMiner not only can solve the problems of previous approaches, but also can scale up well for high-dimensional cases. Evaluations on DBSMiner have been performed using the car and the shuttle databases maintained at the UCI Machine Learning Repository. The results indicate that DBSMiner is effective and can scale up quite linearly with an increasing number of attributes.
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