Research on web user's behaviour data mining based on feature orientation

被引：0
作者：
Zhang H. [1 ]
Jiang X. [1 ]
Zhang F. [1 ]
机构：
[1] Department of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian
来源：
Zhang, Hui (huizhang987@outlook.com) | 1600年 / Inderscience Publishers卷 / 18期
关键词：
Data mining; Feature orientation; Web user's behaviour;
D O I：
10.1504/ijict.2021.10037616
中图分类号：
学科分类号：
摘要：
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. © 2021 Inderscience Enterprises Ltd.. All rights reserved.
引用
收藏
页码：304 / 316
页数：12
相关论文
共 15 条
  • [1] Bui D.T., Ho T.C., Pradhan B., Et al., GIS-based modeling of rain-induced land slides using data mining-based functional trees classifier with adaboost, bagging, and multiboost ensemble frameworks, Environmental Earth Sciences, 75, 14, pp. 1-22, (2016)
  • [2] Carmichael L., Stallabourdillon S., Staab S., Data mining and automated discrimination: A mixed legal/technical perspective, IEEE Intelligent Systems, 31, 6, pp. 51-55, (2016)
  • [3] Elrazek A., Amer M., El Hawary B., Et al., Prediction of HCV vertical transmission: What factors should be optimized using data mining computational analysis, Liver International, 37, 4, pp. 529-533, (2016)
  • [4] Geilhufe R.M., Bouhon A., Borysov S.S., Et al., Three-dimensional organic dirac-line materials due to non-symmorphic symmetry: A data mining approach, Physical Review B, 95, 4, (2017)
  • [5] Guo T., Milanovic J.V., Online identification of power system dynamic signature using PMU measurements and data mining, IEEE Transactions on Power Systems, 31, 3, pp. 1760-1768, (2016)
  • [6] Huang Y., Li T., Luo C., Fujita H., Et al., Matrix-based dynamic updating rough fuzzy approximations for data mining, Knowledge-Based Systems, 119, pp. 273-283, (2017)
  • [7] Lin Y., Research on valuable information data mining in unstructured networks, Computer Simulation, 34, 2, pp. 414-417, (2017)
  • [8] Lorberbaum T., Sampson K.J., Chang J.B., Et al., Coupling data mining and laboratory experiments to discover drug interactions leading to QT prolongation, Journal of the American College of Cardiology, 68, 16, pp. 1756-1764, (2016)
  • [9] Meredig B., Wolverton C., Dissolve the periodic table in cubic zirconia: Data mining to discover chemical trends, Chemistry of Materials, 26, 6, pp. 1985-1991, (2016)
  • [10] Saloot M.A., Idris N., Mahmud R., Et al., Hadith data mining and classification: A comparative analysis, Artificial Intelligence Review, 46, 1, pp. 113-128, (2016)
12