High speed data streams clustering algorithm based on improved SS tree

• In high speed networks, data streams show rapid, bursting and continuous characteristics, which makes real-time clustering of data streams be a difficulty. An improved SS tree structure is designed in this paper to keep the summarized information of data streams. Then, a high speed data streams clustering algorithm based on improved SS tree is proposed. In order to process the bursting streams in time, caching and piggyback mechanisms are used. The chaining buffers in the improved SS tree are used to temporarily store the data stream objects which cannot be processed immediately, and then the contents in buffers will be piggybacked together with the following data. To meet high arrival of data streams, two-phase clustering framework is adopted. Pre-aggregation phase produces local micro-clusters. After that, local micro-clusters take part in the global clustering phase based on the improved SS tree. Experimental results show that the proposed algorithm has better clustering accuracy in high-speed networks. The improved SS tree can effectively cluster high speed data streams and has a good applicability.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datavetenskap (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Sciences (hsv//eng)

Nyckelord

Clustering

Data stream

High-speed network

SS tree

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