منابع مشابه
Data Stream Clustering Algorithms: A Review
Data stream mining has become a research area of some interest in recent years. The key challenge in data stream mining is extracting valuable knowledge in real time from a massive, continuous, dynamic data stream in only a single scan. Clustering is an efficient tool to overcome this problem. Data stream clustering can be applied in various fields such as financial transactions, telephone reco...
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Clustering is an important technique for analysing and interpreting massive quantities of data present on the web. However the sheer volume of data, along with its often dynamic and fast changing nature provide a challenge for traditional clustering approaches. We present a parallel clustering system specifically designed for continuous, real-time clustering of web-scale message data streams. A...
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To solve the problem of distributed data streams clustering, the algorithm DB-DDSC (Density-Based Distribute Data Stream Clustering) was proposed. The algorithm consisted of two stages. First presented the concept of circular-point based on the representative points and designed the iterative algorithm to find the densityconnected circular-points, then generated the local model at the remote si...
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Abstract. Unsupervised identification of groups in large data sets is important for many machine learning and knowledge discovery applications. Conventional clustering approaches (kmeans, hierarchical clustering, etc.) typically do not scale well for very large data sets. In recent years, data stream clustering algorithms have been proposed which can deal efficiently with potentially unbounded ...
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ژورنال
عنوان ژورنال: Artificial Intelligence Review
سال: 2020
ISSN: 0269-2821,1573-7462
DOI: 10.1007/s10462-020-09874-x