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The purpose of communication community structure detection in a network is to cluster weighted complex network. By learning from traditional clustering algorithm, OPTICS, an algorithm is designed to detect communication community and analyze its structure. This algorithm considers the effect and detects communication community based on its communication intensity. The detection result is organized...
Efficient indexing structure is the key of multi-dimension retrieval issue. Lots of indexing structures are failed in the parallelism. This paper presents multi-branch indexing tree (MB-tree), using the nearest neighbor criterion to realize the multi space division of the data sets. The experimental results indicate that the structure is suitable for parallel computing and can significantly improve...
This paper improved the density-based clustering algorithm of data streams and proposed Double Detection Time Strategy The strategy maintained and deleted clusters dynamically. In addition, it preserved potential outlier points with the purpose of high cluster quality and efficiency. Theory and practice show that the improved algorithm possesses good practicality and effectiveness and achieves a higher...
For mining new pattern from evolving data streams, most algorithms are inherited from DenStream framework which is realized via a sliding window. So at the early stage of a pattern emerges, its knowledge points can be easily mistaken as outliers and dropped. In most cases, these points can be ignored, but in some special applications which need to quickly and precisely master the emergence rule of...
Distinguishing potential new cluster data from outliers is a main problem in mining new pattern from evolving data streams. Meanwhile, all the clustering algorithms inherited from CluStream framework are distribution-based learning which are realized via a sliding window, so this problem becomes more obvious. This paper proposes a three-step clustering algorithm, rDenStream, based on DenStream, which...
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