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In the past, we proposed a time series segmentation approach by combining the clustering technique, the Discrete Wavelet Transformation (DWT) and the genetic algorithm to automatically find segments and patterns from a time series. In this paper, we propose a PIP-based evolutionary approach, which uses Perceptually Important Points (PIP) instead of DWT, to effectively adjust the length of subsequences...
Wireless sensor networks are emerged as a new technology in different applications to get information from environment in recent years. On of the most important challenges in this type of networks is energy shortage of sensors. Where as energy restriction, it should be mentioned a fundamental solution to providence energy consumption. The most suitable solution is clustering. In this paper the clustering...
A genetic algorithm-based high-dimensional data clustering technique, called GA-HDclustering, is proposed in this paper. This approach searches feature subspace by genetic algorithms to find the effective clustering feature subspaces. The candidate features and cluster centers are binary encoded, and the degree of feature subspace contributes to subspace clustering is proposed as the fitness function...
Integrating mobility into WSNs can significantly reduce the energy consumption of sensor nodes. However, this may lead to unacceptable data collection latency at the same time. In our previous work, we alleviated the problem under the assumption of a mobile base station (BS). In this paper, we discuss how the problem can be solved when the BS itself is not capable of moving, but it can instead employ...
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