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Network Anomaly Detection plays an importantpart in network security. Among the state-of-the-art approaches, unsupervised anomaly detection is effective when dealing with unlabelled data. However, these approaches also suffer from high false positive rate. We observed that different methods have their own defects and advantages. Inspired by this observation, we provide a new ensemble clustering(NEC)...
In this paper, we propose an efficient and robust gross outlier removal method, called the Conceptual Space based Gross Outlier Removal (CSGOR) method, to remove gross outliers for geometric model fitting. In the proposed method, each data point is mapped to a conceptual space by computing the preference of "good" model hypotheses. In the conceptual space, the distributions of inliers and...
Data sparseness is one of the most challenging problems in collaborative filtering(CF) based recommendation systems. Exploiting social tag information is becoming a popular way to alleviate the problem and improve the performance. To this end, in recent recommendation methods the relationships between users/items and tags are often taken into consideration, however, the correlations among tags from...
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