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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...
In recent years, learning from imbalanced data has attracted growing attention from both academia and industry due to the explosive growth of applications that use and produce imbalanced data. However, because of the complex characteristics of imbalanced data, many real-world solutions struggle to provide robust efficiency in learning-based applications. In an effort to address this problem, this...
In this paper, we propose a self-organizing map approach for spatial outlier detection, the SOMSO method. Spatial outliers are abnormal data points which have significantly distinct non-spatial attribute values compared with their neighborhood. Detection of spatial outliers can further discover spatial distribution and attribute information for data mining problems. Self-Organizing map (SOM) is an...
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