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IDS (Intrusion Detection system) is an active and driving defense technology. This paper mainly focuses on intrusion detection based on data mining. The aim is to improve the detection rate and decrease the false alarm rate, and the main research method is clustering analysis. The algorithm and model of ID are proposed and corresponding simulation experiments are presented. Firstly, a method to reduce...
Despites the great interest caused by social networks in Business Science, their analysis is rarely performed both in a global and systematic way in this field: most authors focus on parts of the studied network, or on a few nodes considered individually. This could be explained by the fact that practical extraction of social networks is a difficult and costly task, since the specific relational data...
In order to improve the efficiency of regression testing, many test selection techniques have been proposed to extract a small subset from a huge test suite, which can approximate the fault detection capability of the original test suite for the modified code. This paper presents a new regression test selection technique by clustering the execution profiles of modification-traversing test cases. Cluster...
Cluster analysis becoming increasingly essential in data mining field, and is mainly used to discover the valuable data distribution and data mode in the potential datum. Based on the pheromone studies on basic clustering model, the theory of information entropy and two classical clustering analysis algorithms, an algorithm of K-means based on the pheromone is presented firstly. The algorithm works...
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