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This paper discusses the second-order mining of the results of data mining. There is still gap between the knowledge which can direct the operation of company and the knowledge got from data mining. We take the knowledge from data mining as primary knowledge and the knowledge from second-order data mining as intelligent knowledge. We discuss the importance of intelligent knowledge and the way to find...
A new kernel-based learning algorithm called kernel affine subspace nearest point (KASNP) approach is proposed in this paper. Inspired by the geometrical explanation of support vector machines (SVMs) and its nearest point problem in convex hulls, we extend the convex hull of each class to its corresponding affine subspace in high dimensional space induced by kernel. In two class affine subspaces,...
This paper introduces a novel pattern classification approach called l1 norm nearest neighbor convex hull (l1 NNCH) approach and applies it for PCA-based face classification. In l1 NNCH, l1 norm distance from a query to a convex hull of a class is defined as the similarity of nearest neighbor rule. Principle component analysis (PCA), as an efficient technology for extracting feature, is applied...
Knowledge or hidden patterns discovered by data mining from large-scale databases has great novelty, which is often unable to be gained from the experts. Given large-scale databases, this paper proposes foundations of intelligent knowledge management. It enables to generate "special" knowledge, called intelligent knowledge base on the hidden patterns created by data mining. Furthermore,...
Recent years have witnessed a large body of research works on mining knowledge from large volume of data to support decision making, where a primary assumption is that the training and test examples come from a same domain (i.e., the target domain). However, this assumption, in reality, is too rigorous to describe the training examples which may come from a different domain to the target domain (i...
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