An efficient fuzzy clustering algorithm for mining user session clusters on web log data
Data mining is extremely vital to get important information from the web. Additionally, web usage mining (WUM) is essential for companies. WUM permits organizations to create rich information related to the eventual fate of their commercial capacity. The utilization of data that is assembled by Web...
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Universitas Komputer Indonesia
2021
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Online Access: | http://irep.iium.edu.my/98927/7/98927_An%20efficient%20fuzzy%20clustering%20algorithm%20for%20mining.pdf http://irep.iium.edu.my/98927/ https://ojs.unikom.ac.id/index.php/injiiscom/article/view/7349/3110 https://doi.org/10.34010/injiiscom.v2i2.7349 |
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my.iium.irep.989272022-07-27T02:43:25Z http://irep.iium.edu.my/98927/ An efficient fuzzy clustering algorithm for mining user session clusters on web log data Mallik, M. A. Zulkurnain, Nurul Fariza TK7885 Computer engineering Data mining is extremely vital to get important information from the web. Additionally, web usage mining (WUM) is essential for companies. WUM permits organizations to create rich information related to the eventual fate of their commercial capacity. The utilization of data that is assembled by Web Usage Mining gives the organizations the capacity to deliver results more compelling to their organizations and expanding of sales. Client access patterns can be mined from web access log information using Web Usage Mining (WUM) techniques. Because there are so many end-user sessions and URL resources, the size of web user session data is enormous. Human communications and non-deterministic browsing patterns increment equivocalness and dubiousness of client session information. The fuzzy set-based approach can solve most of the challenges listed above. This paper proposes an efficient Fuzzy Clustering algorithm for mining client session clusters from web access log information to find the groups of client profiles. In addition, the methodologies to preprocess the net log data as well as data cleanup client identification and session identification are going to be mentioned. This incorporates the strategy to do include choice (or dimensionality decrease) and meeting weight task assignments. Universitas Komputer Indonesia 2021-06-20 Article PeerReviewed application/pdf en http://irep.iium.edu.my/98927/7/98927_An%20efficient%20fuzzy%20clustering%20algorithm%20for%20mining.pdf Mallik, M. A. and Zulkurnain, Nurul Fariza (2021) An efficient fuzzy clustering algorithm for mining user session clusters on web log data. International Journal of Informatics, Information System and Computer Engineering, 2 (2). pp. 80-93. ISSN 2810-0670 E-ISSN 2775-5584 https://ojs.unikom.ac.id/index.php/injiiscom/article/view/7349/3110 https://doi.org/10.34010/injiiscom.v2i2.7349 |
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TK7885 Computer engineering Mallik, M. A. Zulkurnain, Nurul Fariza An efficient fuzzy clustering algorithm for mining user session clusters on web log data |
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Data mining is extremely vital to get important information from the web. Additionally, web usage mining (WUM) is essential for companies. WUM permits organizations to create rich information related to the eventual fate of their commercial capacity. The utilization of data that is assembled by Web Usage Mining gives the organizations the capacity to deliver results more compelling to their organizations and
expanding of sales. Client access patterns can be mined from web access log information using Web Usage Mining (WUM) techniques. Because there are so many end-user sessions and URL resources, the size of web user session data is enormous. Human communications and non-deterministic browsing patterns increment equivocalness and dubiousness of client session information. The fuzzy set-based approach can solve most of the challenges listed above. This paper proposes an efficient Fuzzy Clustering algorithm for mining
client session clusters from web access log information to find the groups of client profiles. In addition, the methodologies to preprocess the net log data as well as data cleanup client identification and session identification are going to be mentioned. This incorporates the strategy to do include choice (or dimensionality decrease) and meeting weight task assignments. |
format |
Article |
author |
Mallik, M. A. Zulkurnain, Nurul Fariza |
author_facet |
Mallik, M. A. Zulkurnain, Nurul Fariza |
author_sort |
Mallik, M. A. |
title |
An efficient fuzzy clustering algorithm for mining user session clusters on web log data |
title_short |
An efficient fuzzy clustering algorithm for mining user session clusters on web log data |
title_full |
An efficient fuzzy clustering algorithm for mining user session clusters on web log data |
title_fullStr |
An efficient fuzzy clustering algorithm for mining user session clusters on web log data |
title_full_unstemmed |
An efficient fuzzy clustering algorithm for mining user session clusters on web log data |
title_sort |
efficient fuzzy clustering algorithm for mining user session clusters on web log data |
publisher |
Universitas Komputer Indonesia |
publishDate |
2021 |
url |
http://irep.iium.edu.my/98927/7/98927_An%20efficient%20fuzzy%20clustering%20algorithm%20for%20mining.pdf http://irep.iium.edu.my/98927/ https://ojs.unikom.ac.id/index.php/injiiscom/article/view/7349/3110 https://doi.org/10.34010/injiiscom.v2i2.7349 |
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