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In this paper, we propose a novel method SOMKE, for kernel density estimation (KDE) over data streams based on sequences of self-organizing map (SOM). In many stream data mining applications, the traditional KDE methods are infeasible because of the high computational cost, processing time, and memory requirement. To reduce the time and space complexity, we propose a SOM structure in this paper to...
We investigate the kernel density estimation (KDE) problem with stream data in this paper. Specifically, we analyze the characteristics of stream data density estimation, and propose an approach based on self-organizing map (SOM) to tackle the challenges of traditional KDE techniques for stream data analysis, such as computational cost, processing time, and memory requirement. Our proposed approach...
This paper proposes a novel feature ranking method, DensityRank, based on kernel estimation on the feature spaces to improve the classification performance. As the availability of raw data in many of today's applications continues to grow at an explosive rate, it is critical to assess the learning capabilities of different features and select the important subset of features to improve learning accuracy...
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