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In this article, a distributed clustering technique, that is suitable for dealing with large data sets, is presented. This algorithm is actually a modified version of the very common k-means algorithm with suitable changes for making it executable in a distributed environment. For large input size, the running time complexity of k-means algorithm is very high and is measured as O(TKN), where K is...
In this paper, we introduce a clustering algorithm for intrusion detection based on WaveCluster algorithm and an entropy-based characteristics screening algorithm. WaveCluster algorithm has a low time complexity when the data are low-dimensional, but on the contrary, the actual network data are high-dimensional. So we reduce the dimension of the network data using characteristics screening before...
Clustering is one of the fundamental data mining tasks. Many different clustering paradigms have been developed over the years, which include partitional, hierarchical, mixture model based, density-based, spectral, subspace, and so on. The focus of this paper is on full-dimensional, arbitrary shaped clusters. Existing methods for this problem suffer either in terms of the memory or time complexity...
Network diameter is one of the important parameters of a network, until now, however, there has not been a perfect algorithm which has a lower time complexity than O(n2) to deal with this problem. As increasingly expanding of network scale and increasing number of nodes and edges, it would spend a lot of time that using Floyd algorithm whose time complexity is defined O(n3) or Breadth-first Search(BFS)...
Outlier detection is an important problem for many domains and has attracted much attention recently. The density-based method LOF is widely used in application. However, the complexity of the method is quadratic to size of the dataset, and it may miss the potential outliers when density distributions in the neighborhood are significantly different. In this paper, we propose a new outlier detection...
Outlier detection is important in many fields. The concept about outlier factor of object is extended to the case of cluster. Based on outlier factor of cluster, a clustering-based outlier detection method, named CBOD, is presented. The method consists of two stages, the first stage cluster dataset by one-pass clustering algorithm and second stage determine outlier cluster by outlier factor. The time...
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