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This paper studies the problem of building Web page classifiers using positive and unlabeled examples, and proposes a more principled technique to solving the problem based on tolerance rough set and Support Vector Machine (SVM). It uses tolerance classes to approximate concepts existed in Web pages and enrich the representation of Web pages, draws an initial approximation of negative example. It then iteratively runs SVM to build classifier which maximizes margins to progressively improve the approximation of negative example. Thus, the class boundary eventually converges to the true boundary of the positive class in the feature space. Experimental results show that the novel method outperforms existing methods significantly. [ABSTRACT FROM AUTHOR]

Due to the uncertainty in accessing Web pages, analysis of Web logs faces some challenges. Several rough $\mathnormal{k}$-means cluster algorithms have been proposed and successfully applied to Web usage mining. However, they did not explain why rough approximations of these cluster algorithms were introduced. This paper analyzes the characteristics of the data in the boundary areas of clusters, and then a rough $\mathnormal{k}$-means cluster algorithm based on a reasonable rough approximation (RKMrra) is proposed. Finally RKMrra is applied to Web access logs. In the experiments RKMrra compares to Lingras and West algorithm and Peters algorithm with respect to five characteristics. The results show that RKMrra discovers meaningful clusters of Web users and its rough approximation is more reasonable. [ABSTRACT FROM AUTHOR]

The quality of character image plays an important role for the performance of character recognition system. However there is no good way to measure the recognition difficulty of a given character image. For the given character image with unknown quality, it is improper that apply the single character database to recognize it by the same feature and the same classifier. This paper proposed a novel approach for multi-resolution character recognition whose feature is extracted directly from gray-scale image and classification is adaptive classification which adaptively selects the appropriate character database and classifiers by evaluating the image quality of the input character. A resolution evaluation algorithm based on gray distribution feature was proposed to decide the adaptive classification weights for the classifiers, which make the classification have the higher probability of being the correct decision. Experiment results demonstrate the proposed approach highly improved the performance of character recognition system. [ABSTRACT FROM AUTHOR]

Skowron's discernibility matrix is one of representative approaches in computing relative core and relative reducts, while redundant information is also involved. To decrease the complexity of computation, the idea of granular computing is applied to lower the rank of discernibility matrix. In addition, the absorptivity based on bit-vector computation is proposed to simplify computation of relative core and relative reducts. Keywords: Rough set, discernibility matrix, granular computing, absorptivity. [ABSTRACT FROM AUTHOR]

The theory of rough sets is an extension of the set theory, for the study of intelligent systems characterized by insufficient and incomplete information. Since proposed by Pawlak, rough sets have evoked a lot of research. Theoretic study has included algebra aspect of rough sets. In paper [1] the concept of rough group and rough subgroup was introduced, but with some deficiencies remaining. In this paper, we intend to make up for these shortages, improve definitions of rough group and rough subgroup, and prove their new properties. [ABSTRACT FROM AUTHOR]