Currently, feature selection faces a huge challenge that no single feature selection method
can effectively deal with various data sets for all real cases. Ensemble learning is a potential …

Abstract Knowledge graph (KG) modeling constructs a connected network of hazard/fault
events by drawing on historical reports, helping extract risk factors and propagation features …

Exploratory data analysis (EDA) using data clustering is extremely important for
understanding the basic characteristics of a novel data set before developing complex …

The evidence accumulation model is an approach for collecting the information of base
partitions in a clustering ensemble method, and can be viewed as a kernel transformation …

The clustering ensemble method has attracted much attention because it can improve the
stability and robustness of single clustering methods. Among them, similarity-matrix-based …

In this paper, a novel sparse recovery based reversible data hiding (RDH) method using the
human visual system (HVS) is presented. To improve the low accuracy of existing predictors …

Finding the cluster structure is essential for analyzing self-organized networking structures,
such as social networks. In such problems, a wide variety of distance measures can be …

Intensity inhomogeneity and the bias field often occur in real-world images, which cause
considerable difficulties in image segmentation. This paper presents a local region-based …

由No Free Lunch 理论可知, 没有一种聚类算法可完美的解决所有问题. 算法推荐是解决此问题
的一种有效手段, 其核心是数据集相似性的度量. 因此提出了一种计算数据集相似性的新方法 …

Abstract The Western Han were a dynasty of Ancient China with a history of two hundred
years. The empire's constitution and development were often accompanied by the …