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Because of data uncertainty and time and space limitations in clustering process, clustering uncertain data stream becomes a very challenging task. A lot of processing algorithms has been proposed continuously. This paper reviews clustering algorithms of uncertain data stream and dissects the advantages and disadvantages of these algorithms for future development.
Data anonymization techniques are the main way to achieve privacy protection, and as a classical anonymity model, K-anonymity is the most effective and frequently-used. But the majority of K-anonymity algorithms can hardly balance the data quality and efficiency, and ignore the privacy of the data to improve the data quality. To solve the problems above, by introducing the concept of “diameter” and...
In recent years, data dissemination privacy protection issues received extensive attention. A variety of privacy preserving anonymity models and technology, such as generalization, anatomy and slicing have been proposed. We present a new technique - overlapping slicing, it handles data attributes mainly based on the idea of fuzzy clustering. And we present a linear algorithm of processing data with...
CluStream is a representative data stream cluster algorithm, however, it is not able to find arbitrary clusters in shape and deal with boundary points effectively. A new algorithm which is based on active grid density AGD-Stream is proposed to solve these problems in this paper. The algorithm adopts a density decaying technology to capture the dynamic of data stream, and extracts the boundary point...
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