Title: Algorithm of key data ensemble clustering and approximate analysis in cloud computing

Authors: Xia Wendong; Liu Yuanfeng; Chen Deli

Addresses: College of Computer, Jiaying University, Meizhou Guangdong, 514087, China ' Guangdong Ji Tong Information Development Co., Ltd, Guangzhou Guangdong, 510632, China ' College of Computer, Jiaying University, Meizhou Guangdong, 514087, China

Abstract: One collaborative data fusion recommendation algorithm is SFS-TOPSIS based on customer satisfaction degree. First, it starts from calculation efficiency and recommended precision angle that improves service recommendation algorithm, makes real-time updating and algorithm improvement for it by combining time-varying weight method TOPSIS fusion algorithm and designs a collaborative data fusion recommendation algorithm based on customer satisfaction degree. Second, for the problem of inadequate definition of traditional similarity for resolution, improvements have been made based on user evaluation confidence. Last, time-varying weight method has been adopted to improve standard TOPSIS fusion, improve time-varying attribute of TOPSIS decision fusion and realise effective attribute fusion of user similarity data; through making simulation comparison on two standard testing sets of MovieLens and BookCrossing, it indicates that the service recommendation performance of SFS-TOPSIS is superior. The proposed SFS-TOPSIS algorithm can improve service recommendation accuracy effectively and it is with certain application value.

Keywords: big data; similarity; approximate analysis; clustering; cloud computing; decision recommendation.

DOI: 10.1504/IJRIS.2017.090038

International Journal of Reasoning-based Intelligent Systems, 2017 Vol.9 No.3/4, pp.177 - 184

Available online: 21 Feb 2018 *

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