Research on web user's behaviour data mining based on feature orientation
by Hui Zhang; Xiaoling Jiang; Fa Zhang
International Journal of Information and Communication Technology (IJICT), Vol. 18, No. 3, 2021

Abstract: In view of the problems of long mining time and high error rate in the existing network user behaviour data mining methods, a network user behaviour data mining method based on feature preference is proposed. The interaction relationship in social network is analysed as the constraint condition of feature selection. Laplasian operator is used to construct the feature selection model of network user correlation and to quantify the relationship between users. The improved ant colony algorithm is used to obtain the optimal feature subset to realise the network user behaviour data mining. The experimental results show that, compared with the traditional methods, the mining time of the proposed method is shorter and the mining error rate is lower under the condition of low and high excavation strength, which verifies the effectiveness of the proposed method.

Online publication date: Mon, 10-May-2021

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