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Most anomaly detection methods can not be fit for the changing and complex network. High noise and updating normality profiles not in time will lead to high false alarm rate. In this paper, a new anomaly detection algorithm using improved hierarchy clustering, called ADIHC, is proposed in this paper. It applies an improved hierarchy clustering tree to organize clusters which are obtained by density-based...
Mining frequent patterns is an important task for knowledge discovery, which discovers the groups of items appearing always together excess of a user specified threshold. A famous algorithm for mining frequent patterns is FP-Growth which constructs a structure called FP-tree and recursively mines frequent patterns from this structure by building conditional FP-trees. However, It is costly to recursively...
Many approaches have been proposed for mining frequent pattern. However, either the search space or memory space is huge, such that the performance for the previous approach degrades when the database is massive or the threshold for mining frequent patterns is low. In this paper, we propose an algorithm for mining frequent patterns. Our algorithm only needs to construct a FP-tree and traverse each...
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