A bit-object-based method for mining maximum frequent patterns in intensive cloud computing data

Article type: Research Article

Authors: Chen, Chen^{; *} | Yang, Li | Jia, Xunan

Affiliations: Library Heilongjiang Bayi Agricultural University, Daqing 163319, China

Correspondence: [*] Corresponding author. E-mail: chenwen@mls.sinanet.com.

Abstract: In order to overcome the problems of poor timeliness and low accuracy of mining existing in traditional methods, this paper designs a bit-object based maximum frequent pattern mining method for intensive cloud computing data. After judging the support number according to the bit object of the maximum frequent pattern, the intensive cloud computing data is accurately collected according to the difference between the load value of cloud data and the true value of load, so as to improve the accuracy of subsequent mining results, and then the maximum frequent pattern of data is accurately mined by combining the bit object. Experimental results show that the maximum time to generate mining results is only 4.6 s, the maximum bit error rate of output results is only 7%, and the maximum memory occupancy is only 3.90%. The above results show that this method is more suitable for practical excavation.

Keywords: Intensive cloud computing data, maximum frequency mode, bit object, data acquisition, frequent tree, frequent pattern mining

DOI: 10.3233/WEB-210461

Journal: Web Intelligence, vol. 19, no. 1-2, pp. 125-133, 2021