Noise Reduction In Web Data: A Learning Approach Based On Dynamic User Interests
Main Article Content
Abstract
One of the prominent challenges internet operators encounter is the abundance of extraneous material inside web content, hence impeding the efficient retrieval of relevant information aligned with their evolving interests. The present state of affairs. In academic research, noise is commonly defined as any extraneous data that does not contribute to the intended analysis or study objectives. This study aims to analyse the primary webpage and suggest noise reduction tools for online data. The primary emphasis is on the reduction of noise about the content and its associated factors. The arrangement or organisation of data on the internet. In this paper, including some data inside a dataset may not be universally applicable or appropriate. The web page's primary content pertains to the user's specific interests, while extraneous info is minimised. Noise can be perceived as disruptive or unwanted sound by an individual. Hence, the acquisition of noisy online data and the allocation of resources to user requests ensure not just a decrease in noise levels. There is an observed correlation between the level indicated in a user profile on the web and a reduction in the occurrence of valuable information loss. The inclusion of information consequently enhances the calibre of an online user profile. The phenomenon of noise refers to unwanted or disruptive sounds that can have negative effects on individuals and the Web Data Learning (NWDL) tool/algorithm exhibits the capacity to acquire knowledge. The proposal suggests the use of noise in web user profiles to enhance data privacy. The work that has suggested the removal of noise data in the context of dynamic user behaviour is being considered. The topic of interest is being discussed. To ascertain the efficacy of the proposed study, A presentation of an experimental design arrangement is provided. The results were achieved in contrast to the presently employed algorithms utilised in the context of noisy online data. The reducing process. The experimental findings indicate that the proposed study examines the dynamic evolution of user interest before the removal of extraneous data. The proposed study makes a significant contribution to Enhancing the calibre of an online user profile through the reduction of content volume. The elimination of noise results in the removal of beneficial information.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
J. Srivastava, R. Cooley, M. Deshpande, and P.-N. Tan, ‘Web Usage Mining: Discovery and Applications of Usage Patterns from Web Data’, SIGKDD Explor Newsl, vol. 1, no. 2, pp. 12–23, Jan. 2000.
M. Jafari, F. SoleymaniSabzchi, and S. Jamali, ‘Extracting Users’Navigational Behavior from Web Log Data: a Survey’, J. Comput. Sci. Appl. J. Comput. Sci. Appl., vol. 1, no. 3, pp. 39–45, Jan. 2013.
N. Soni and P. K. Verma, ‘A Survey On Web Log Mining And Pattern Prediction’, Int. J. Adv. Technol. Eng. Sci.-2348-7550.
T. R. Ramesh and C. Kavitha, ‘Web user interest prediction framework based on user behaviour for dynamic websites’, Life Sci. J., vol. 10, no. 2, pp. 1736–1739, 2013.
L. Yi, B. Liu, and X. Li, ‘Eliminating Noisy Information in Web Pages for Data Mining’, in Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 2003, pp. 296–305.
A. Dutta, S. Paria, T. Golui, and D. K. Kole, ‘Structural analysis and regular expressions based noise elimination from web pages for web content mining’, in 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI), 2014, pp. 1445–1451.
G. D. S. Jayakumar and B. J. Thomas, ‘A new procedure of clustering based on multivariate outlier detection’, J. Data Sci., vol. 11, no. 1, pp. 69–84, 2013.
V. Chitraa and A. S. Thanamani, ‘Web Log Data Analysis by Enhanced Fuzzy C Means Clustering’, Int. J. Comput. Sci. Appl., vol. 4, no. 2, pp. 81–95, Apr. 2014.
L. K. Joshila Grace, V. Maheswari, and D. Nagamalai, ‘Analysis of Web Logs And Web User In Web Mining’, Int. J. Netw. Secure. Its Appl., vol. 3, no. 1, pp. 99–110, Jan. 2011.
S. Gauch, M. Speretta, A. Chandramouli, and A. Micarelli, ‘User profiles for personalized information access’, in The adaptive web, Springer, 2007, pp. 54–89.
P. Peñas, R. del Hoyo, J. Vea-Murguía, C. González, and S. Mayo, ‘Collective Knowledge Ontology User Profiling for Twitter – Automatic User Profiling’, in 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2013, vol. 1, pp. 439–444.
S. Kanoje, S. Girase, and D. Mukhopadhyay, ‘User profiling trends, techniques and applications’, ArXiv Prepr. ArXiv150307474, 2015.
H. Kim and P. K. Chan, ‘Implicit indicators for interesting web pages’, 2005.
J. Xiao, Y. Zhang, X. Jia, and T. Li, ‘Measuring similarity of interests for clustering Web-users’, in Proceedings 12th Australasian Database Conference. ADC 2001, 2001, pp. 107–114.
H. Liu and V. Kešelj, ‘Combined Mining of Web Server Logs and Web Contents for Classifying User Navigation Patterns and Predicting Users’ Future Requests’, Data Knowl Eng, vol. 61, no. 2, pp. 304–330, May 2007.
O. Nasraoui, M. Soliman, E. Saka, A. Badia, and R. Germain, ‘A Web Usage Mining Framework for Mining Evolving User Profiles in Dynamic Web Sites’, IEEE Trans. Knowl. Data Eng., vol. 20, no. 2, pp. 202–215, Feb. 2008.
T. Htwe and N. S. M. Kham, ‘Extracting data region in a web page by removing noise using DOM and neural network’, in 3rd International Conference on Information and Financial Engineering, 2011.
R. P. Velloso and C. F. Dorneles, ‘Automatic Web Page Segmentation and Noise Removal for Structured Extraction using Tag Path Sequences’, J. Inf. Data Manag., vol. 4, no. 3, p. 173, Sep. 2013.
Y. L. Sulastri, A. B. Ek, and L. L. Hakim, ‘Developing Students’ Interest by Using Weblog Learning’, GSTF Int. J. Educ. Vol1 No2, vol. 1, no. 2, Nov. 2013.
A. Nanda, R. Omanwar, and B. Deshpande, ‘Implicitly Learning a User Interest Profile for Personalization of Web Search Using Collaborative Filtering’, in 2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT), 2014, vol. 2, pp. 54–62.
J. Onyancha, V. Plekhanova, and D. Nelson, ‘Noise Web Data Learning from a Web User Profile: Position Paper’, in Proceedings of the World Congress on Engineering, 2017, vol. 2.
N. Narwal, ‘Improving web data extraction by noise removal’, in Fifth International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom 2013), 2013, pp. 388–395