Research on complex attribute big data classification based on iterative fuzzy clustering algorithm

Article type: Research Article

Authors: Qian, Li^{; *}

Affiliations: School of Digital Information Technology, Zhejiang Technical Institute of Economics, Hangzhou 310018, China. E-mail: [email protected]

Correspondence: [*] Corresponding author. E-mail: [email protected].

Abstract: In order to overcome the low classification accuracy of traditional methods, this paper proposes a new classification method of complex attribute big data based on iterative fuzzy clustering algorithm. Firstly, principal component analysis and kernel local Fisher discriminant analysis were used to reduce dimensionality of complex attribute big data. Then, the Bloom Filter data structure is introduced to eliminate the redundancy of the complex attribute big data after dimensionality reduction. Secondly, the redundant complex attribute big data is classified in parallel by iterative fuzzy clustering algorithm, so as to complete the complex attribute big data classification. Finally, the simulation results show that the accuracy, the normalized mutual information index and the Richter’s index of the proposed method are close to 1, the classification accuracy is high, and the RDV value is low, which indicates that the proposed method has high classification effectiveness and fast convergence speed.

Keywords: Iterative fuzzy clustering algorithm, complex attribute, big data, data classification

DOI: 10.3233/WEB-210463

Journal: Web Intelligence, vol. 19, no. 1-2, pp. 147-158, 2021