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Selecting the appropriate number of clusters and distinguishing partially overlapping and irregular data are two important problems in clustering. Hierarchical clustering provides a good solution to them. Similarity measure is the key of controlling the iterative process of hierarchical clustering. In this paper, we give a definition of overlap similarity(More)
Intrusion detection system (IDS) is an important component of computer network security, while clustering analysis is a common unsupervised anomaly detection method. However, it is difficult for the single clustering algorithm to get the great effective detection, and the data of intrusion attacks is anomalistic normally. This paper presents an unsupervised(More)
Compared with the single clustering algorithm, Clustering Ensembles are deemed to be more robust and accurate, with combining multiple partitions of the given data into a single clustering solution of better quality. In this paper, we proposed a new Clustering Ensemble algorithm based on Fuzzy K Nearest Neighbor (FKNNCE) to generate the similarity matrix of(More)
In this paper, a new technology is proposed to solve anomaly detection problems of the high false positive rate or hard to build the model of normal behavior, etc. What our technology based on is the similarity between outliers and intrusions. So we proposed a new outlier mining algorithm based on index tree to detect intrusions. The algorithm improves on(More)
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