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Journal articles on the topic 'Web mining'

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Published: 4 June 2021
Last updated: 5 March 2023

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1

Stoffel, Kilian. "Web + Data Mining = Web Mining." HMD Praxis der Wirtschaftsinformatik 46, no. 4 (August 2009): 6–20. http://dx.doi.org/10.1007/bf03340377.

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2

Walther, Ralf. "Web Mining." Informatik-Spektrum 24, no. 1 (February 20, 2001): 16–18. http://dx.doi.org/10.1007/s002870100145.

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3

Agyapong, Kwame, J. B. Hayfron Acquah, and M. Asante. "AN OPTIMIZED PAGE RANK ALGORITHM WITH WEB MINING, WEB CONTENT MINING AND WEB STRUCTURE MINING." International Journal of Engineering Technologies and Management Research 4, no. 8 (February 1, 2020): 22–27. http://dx.doi.org/10.29121/ijetmr.v4.i8.2017.91.

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With the rapid increase in internet technology, users get easily confused in large hypertext structure. The primary goal of the web site owner is to provide the relevant information to the users to fulfill their needs. In order to achieve this goal, they use the concept of web mining. Web mining is used to categorize users and pages by analyzing the users‟ behaviour, the content of the pages, and the order of the URLs that tend to be accessed in order. Most of the search engines are ranking their search results in response to users' queries to make their search navigation easier. With a web browser, one can view web pages that may contain text, images, videos, and other multimedia, and navigate between them via hyperlinks. It is very difficult for a user to find the high quality information which he wants. Page Ranking algorithm is needed which provide the higher ranking to the important pages. In this paper, we discuss the improvement of Page ranking algorithm to provide the higher ranking to important pages. Most of the search engines are ranking their search results in response to user’s queries to make their search navigations easier.
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4

Vartak, Amey. "Web Personalization using Web Mining." International Journal for Research in Applied Science and Engineering Technology 6, no. 4 (April 30, 2018): 86–89. http://dx.doi.org/10.22214/ijraset.2018.4019.

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5

Eirinaki, Magdalini, and Michalis Vazirgiannis. "Web mining for web personalization." ACM Transactions on Internet Technology 3, no. 1 (February 2003): 1–27. http://dx.doi.org/10.1145/643477.643478.

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6

Skaria, Bibu, Dr Eldhose T John, and P. X. Shajan. "Literature Review on Web Mining." Bonfring International Journal of Data Mining 6, no. 1 (January 31, 2016): 04–06. http://dx.doi.org/10.9756/bijdm.8127.

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7

Hippner, Hajo, Melanie Merzenich, and Klaus D. Wilde. "Web Usage Mining." WiSt - Wirtschaftswissenschaftliches Studium 31, no. 2 (2002): 105–10. http://dx.doi.org/10.15358/0340-1650-2002-2-105.

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8

Ahmed, Moiz Uddin, and Amjad Mahmood. "Web Usage Mining." International Journal of Technology Diffusion 3, no. 3 (July 2012): 1–12. http://dx.doi.org/10.4018/jtd.2012070101.

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The technological revolutions have opened up new ways of information and communication. The Internet is growing as a vital source of information in this modern era of technology. The ever increasing volume of information through WWW is creating complexity in the design, development and deployment of WWW. It has become important for the organizations to analyze the usage of their web sites. The web usage analysis may help the organizations not only to monitor the load on their websites and cater for the needs of their potential clients but also enhance their web services and restructure the organization to better serve their clients. Web mining has emerged as important research areas used to discover information which can be utilized for improvement of websites. Allama Iqbal Open University (AIOU) is one of the largest open and distant university of the world. Due to unique philosophy of open and distant learning, AIOU has been providing useful information online through its website. It is an active website which is flooded with huge flow of information. This paper presents web usage analysis of AIOU website and provides statistical analysis of the usage patterns. It presents how the results were used not only to enhance the web contents and services but also discusses how these results helped the university to allocate and reallocate its resources. The reallocation was used to improve efficiency and processes of the university in order to better serve its clients.
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9

Nasraoui, Olfa. "Web data mining." ACM SIGKDD Explorations Newsletter 10, no. 2 (December 20, 2008): 23–25. http://dx.doi.org/10.1145/1540276.1540281.

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10

Stumme, Gerd, Andreas Hotho, and Bettina Berendt. "Semantic Web Mining." Journal of Web Semantics 4, no. 2 (June 2006): 124–43. http://dx.doi.org/10.1016/j.websem.2006.02.001.

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11

Srivastava, Jaideep, Robert Cooley, Mukund Deshpande, and Pang-Ning Tan. "Web usage mining." ACM SIGKDD Explorations Newsletter 1, no. 2 (January 2000): 12–23. http://dx.doi.org/10.1145/846183.846188.

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12

Kosala, Raymond, and Hendrik Blockeel. "Web mining research." ACM SIGKDD Explorations Newsletter 2, no. 1 (June 2000): 1–15. http://dx.doi.org/10.1145/360402.360406.

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13

Lingaraju, Dr G. M., and Dr S. Jagannatha. "Review of Web Page Classification and Web Content Mining." Journal of Advanced Research in Dynamical and Control Systems 11, no. 10 (October 31, 2019): 142–47. http://dx.doi.org/10.5373/jardcs/v11i10/20193017.

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14

Thiyagarajan, V. S. "Web Data mining-A Research area in Web usage mining." IOSR Journal of Computer Engineering 13, no. 1 (2013): 22–26. http://dx.doi.org/10.9790/0661-1312226.

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15

Panchal, Ashish. "A Survey of Web Mining and Various Web Mining Techniques." International Journal for Research in Applied Science and Engineering Technology 7, no. 9 (September 30, 2019): 933–39. http://dx.doi.org/10.22214/ijraset.2019.9130.

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16

Tripathi, Rajni, Munesh Chandra Trivedi, and Shraddha Tripathi. "Web Usage Mining: A Fact Finding Approach in Web Mining." International Journal of Computer Trends and Technology 12, no. 2 (June 25, 2014): 99–103. http://dx.doi.org/10.14445/22312803/ijctt-v12p119.

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17

Chen, Zheng, Liu Wenyin, Feng Zhang, Mingjing Li, and Hongjiang Zhang. "Web mining for Web image retrieval." Journal of the American Society for Information Science and Technology 52, no. 10 (2001): 831–39. http://dx.doi.org/10.1002/asi.1132.

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18

Nijat Babayev, Nijat Babayev. "WEB MINING: DATA MINING ON THE INTERNET." PAHTEI-Procedings of Azerbaijan High Technical Educational Institutions 23, no. 12 (December 19, 2022): 182–93. http://dx.doi.org/10.36962/pahtei23122022-182.

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In the article“Web Mining: Data Mining on the Web” 3 different components of Web Mining have been described and mainly Web Usage Mining has been discussed in detail and analyzed with examples. The general theme of the article is clarified by giving such sub-topics: Difficulties in analyzing data from the Internet, Stages of Web-Mining, Analysis of the use of Web resources (Web Usage Mining), Web Server Log Files and etc. General Relationships Between Web Mining Categories and Data Mining Tasks are shown. At the end, in the Conclusion, it is shown that to solve such problems, Data Mining technology is used. This name refers to a set of methods that allow us to extract useful information according to certain rules from the amount of collected data in which this information is implicitly contained. In this case, both statistical and intellectual processing methods are used. Keywords: Web-Mining, Data Mining Methods, Data from the Internet, Web Usage Mining, User Session Identification.
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19

Sneka, G., H. Sharmila, and R. Banumathi. "De-Identification Technology Involves Web Mining." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (April 30, 2018): 1502–5. http://dx.doi.org/10.31142/ijtsrd11484.

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20

Chomboon, K., N. Kaoungku, K. Kerdprasop, and N. Kerdprasop. "Data Mining in Semantic Web Data." International Journal of Computer Theory and Engineering 6, no. 6 (December 2014): 472–75. http://dx.doi.org/10.7763/ijcte.2014.v6.912.

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21

S, Vishesh, Manu Srinath, Supriya Ramarao Prasanna, and Supriya Yadati Narasimhulu. "Data Mining, Internet Marketing and Web Mining." IJARCCE 6, no. 3 (March 30, 2017): 507–9. http://dx.doi.org/10.17148/ijarcce.2017.63117.

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22

Kumar, S. Sundeep, and Mahesh Kumar Singh. "Web Pattern Analysis Using Web Structure Mining." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 5 (May 30, 2017): 558–63. http://dx.doi.org/10.23956/ijarcsse/sv7i5/0274.

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23

Khatoon, Asfiya, and Kuldeep Jaiswal. "Web Page Ranking using Web Usage Mining." IJARCCE 6, no. 4 (April 30, 2014): 807–15. http://dx.doi.org/10.17148/ijarcce.2017.64150.

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24

., Monika Soni. "FRAMEWORK FOR WEB PERSONALIZATION USING WEB MINING." International Journal of Research in Engineering and Technology 01, no. 02 (February 25, 2012): 152–57. http://dx.doi.org/10.15623/ijret.2012.0102013.

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25

Qiang Yang and H. H. Zhang. "Web-log mining for predictive web caching." IEEE Transactions on Knowledge and Data Engineering 15, no. 4 (July 2003): 1050–53. http://dx.doi.org/10.1109/tkde.2003.1209022.

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26

Spiliopoulou, Myra. "Web usage mining for Web site evaluation." Communications of the ACM 43, no. 8 (August 2000): 127–34. http://dx.doi.org/10.1145/345124.345167.

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27

Satokar, Kavita D., and S. Z. Gawali. "Web Search Result Personalization Using Web Mining." International Journal of Computer Applications 2, no. 5 (June 10, 2010): 29–32. http://dx.doi.org/10.5120/665-933.

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28

Chen, Hsinchun, and Michael Chau. "Web mining: Machine learning for web applications." Annual Review of Information Science and Technology 38, no. 1 (September 22, 2005): 289–329. http://dx.doi.org/10.1002/aris.1440380107.

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29

Rettinger, Achim, Uta Lösch, Volker Tresp, Claudia d’Amato, and Nicola Fanizzi. "Mining the Semantic Web." Data Mining and Knowledge Discovery 24, no. 3 (February 10, 2012): 613–62. http://dx.doi.org/10.1007/s10618-012-0253-2.

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30

neet, Av, and Hardeep Singh. "Web Data Mining: Survey." International Journal of Engineering Trends and Technology 10, no. 3 (April 25, 2014): 144–47. http://dx.doi.org/10.14445/22315381/ijett-v10p228.

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31

Engler, Joseph, and Andrew Kusiak. "Web Mining for Innovation." Mechanical Engineering 130, no. 11 (November 1, 2008): 38–40. http://dx.doi.org/10.1115/1.2008-nov-1.

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This article reviews a way to automatically mine the Web for innovative requirements. Websites now commonly host user reviews filled with opinions about a product’s strengths and weaknesses. Publication of user reviews is so ingrained in the Web that it has spawned an entire field of study known as collaborative intelligence. Collaborative intelligence gathers the collective reasoning of multiple users to achieve some goal. While user and expert reviews offer a wealth of information about the needs and desires of the market, they are not the only source of requirements for innovation. Much the same method can be used to search patent databases for the same type of product attributes and requirements for innovation. Patent databases provide both complete and summary descriptions of already-envisioned inventions and offer a great deal of information about trends in current innovation. Requirements for innovation are changing in time. To keep up with these changes and to aid in the market acceptance of an idea, the mining of current requirements for innovation is crucial.
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32

Omitola, Tope, Sebastián A. Ríos, and John G. Breslin. "Social Semantic Web Mining." Synthesis Lectures on the Semantic Web: Theory and Technology 5, no. 1 (January 19, 2015): 1–154. http://dx.doi.org/10.2200/s00623ed1v01y201412wbe010.

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33

Patil, Prof (Mrs) Manisha R. "HYBRID WEB MINING FRAMEWORK." IOSR Journal of Computer Engineering 2, no. 5 (2012): 34–37. http://dx.doi.org/10.9790/0661-0253437.

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34

Ma, Yiming, Bing Liu, and Ching Kian Wong. "Web for data mining." ACM SIGKDD Explorations Newsletter 2, no. 1 (June 2000): 16–23. http://dx.doi.org/10.1145/360402.360408.

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35

Wang, Pu. "Research on the Mechanism of Web Data Mining in Electronic Commerce Application." Applied Mechanics and Materials 687-691 (November 2014): 3003–6. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3003.

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At present, the growth of the Internet has brought us a vast amount of information that we can hardly deal with. To solve the flood of information, various data mining systems have been created to assist and augment this natural social process. Data minig recommender systems have been developed to automate the recommendation process. Data mining recommender systems can be found at many electronic commerce applications. In this paper, a recommendation mechanism of web data mining in electronic commerce application is given. Then, presents the workflow of the web data mining in electronic commcer. Lastly, the usage of the tools of web data mining is described.
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36

Yau, Ng Qi, and Wan Zainon. "UNDERSTANDING WEB TRAFFIC ACTIVITIES USING WEB MINING TECHNIQUES." International Journal of Engineering Technologies and Management Research 4, no. 9 (February 1, 2020): 18–26. http://dx.doi.org/10.29121/ijetmr.v4.i9.2017.96.

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Web Usage Mining is a computational process of discovering patterns in large data sets involving methods using the artificial intelligence, machine learning, statistical analysis and database systems with the goal to extract valuable information from accessing server logs of World Wide Web data repositories and transform it into an understandable structure for further understanding and use. Main focus of this paper will be centered on exploring methods that expedites the log mining process and present the result of log mining process through data visualization and compare data-mining algorithms. For the comparison between classification techniques, precision, recall and ROC area are the correct measures that are used to compare algorithms. Based on this study it shows that Naïve Bayes and Bayes Network are proven to be the best algorithms for that.
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37

Harika, B., and T. Sudha. "Extraction of Knowledge from Web Server Logs Using Web Usage Mining." Asian Journal of Computer Science and Technology 8, S3 (June 5, 2019): 12–15. http://dx.doi.org/10.51983/ajcst-2019.8.s3.2113.

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Information on internet increases rapidly from day to day and the usage of the web also increases, thus there is the need to discover interesting patterns from web. The process used to extract and mine useful information from web documents by using Data Mining Techniques is called Web Mining. Web Mining is broadly classified in to three types namely Web Content Mining, Web Structure Mining and Web Usage Mining. In this paper our focus is mainly on Web Usage Mining, where we are applying the data mining techniques to analyse and discover interesting knowledge from the Web Usage data. The activities of the user are captured and stored at different levels such as server level, proxy level and user level called as Web Usage Data and the usage data stored at server side is Web Server Log, where it records the browsing behavior of users and their requests based on the user clicks. Web server Log is a primary source to perform Web Usage Mining. This paper also brings in to discussion of various existing pre-processing techniques and analysis of web log files and how clustering is applied to group the users based on the browsing behavior of users on their interested contents.
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38

Eldhose T John, Dr, Bibu Skaria, and P. X. Shajan. "An Overview of Web Content Mining Tools." Bonfring International Journal of Data Mining 6, no. 1 (January 31, 2016): 01–03. http://dx.doi.org/10.9756/bijdm.8126.

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39

Joshila Grace, L. K., V. Maheswari, and Dhinaharan Nagamalai. "Analysis of Web Logs And Web User In Web Mining." International Journal of Network Security & Its Applications 3, no. 1 (January 28, 2011): 99–110. http://dx.doi.org/10.5121/ijnsa.2011.3107.

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40

Chai, Chun Lai. "A Heuristic Mining Algorithm Using Web Hyperlink Structure." Advanced Materials Research 108-111 (May 2010): 11–16. http://dx.doi.org/10.4028/www.scientific.net/amr.108-111.11.

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Web mining aims to discover useful information or knowledge from the Web hyperlink structure, page content and usage log. Based on the primary kind of data used in the mining process, Web mining tasks are categorized into three main types: Web structure mining, Web content mining and Web usage mining. Following is what they do on Web Data Mining. This paper proposed a heuristic mining algorithm.
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41

M., Esraa, A. F., and Hanan E. "Web Mining Techniques to Block Spam Web Sites." International Journal of Computer Applications 181, no. 8 (August 14, 2018): 36–42. http://dx.doi.org/10.5120/ijca2018917622.

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42

Mathan Nagan, B. K., and T. Mahalakshmi. "Efficient Web Page Mining for Dynamic Web Site." Shanlax International Journal of Arts, Science and Humanities 7, no. 2 (October 1, 2019): 98–102. http://dx.doi.org/10.34293/sijash.v7i2.822.

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A huge portion of the organizations have the sites for their business. A huge portion of the clients of the association register their subtleties as client profiles. These client profiles have the individual subtleties and their fascinating propensities for the client. At the point when the client visits our sites the log record is made in the server. By partner the client profiles and web log record we can discover the much of the time visited clients. From the frequently visited client, we can discover when they are visited by grouping the client profiles with web log records. In our work we disclose how to get "who" the clients were, "what" they took a gander at, and "how their interests changed with time, "when" they visit which are all significant inquiries in Customer Relationship Management (CRM). In our examination we present bunching the client profiles. We additionally depict how they found client profiles can be advanced with unequivocal data.
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43

Agyemang, Malik, Ken Barker, and Reda Alhajj. "Web outlier mining: Discovering outliers from web datasets1." Intelligent Data Analysis 9, no. 5 (November 3, 2005): 473–86. http://dx.doi.org/10.3233/ida-2005-9505.

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44

Picariello, Antonio, and Carlo Sansone. "A web usage mining algorithm for web personalization." Intelligent Decision Technologies 2, no. 4 (December 24, 2008): 219–30. http://dx.doi.org/10.3233/idt-2008-2403.

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45

Chen, Hsinchun, Xin Li, Michael Chau, Yi-Jen Ho, and Chunju Tseng. "Using Open Web APIs in Teaching Web Mining." IEEE Transactions on Education 52, no. 4 (November 2009): 482–90. http://dx.doi.org/10.1109/te.2008.930509.

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46

Ghazanfaripoor, Hamed, Ali Harounabadi, and Amir Sabaghmolahoseini. "Users’ recognition in web using web mining techniques." Management Science Letters 3, no. 6 (June 1, 2013): 1707–12. http://dx.doi.org/10.5267/j.msl.2013.05.014.

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47

Pandya, Rutvija. "Web Usage Mining with Personalization on Social Web." International Journal of Engineering Trends and Technology 29, no. 6 (November 25, 2015): 325–28. http://dx.doi.org/10.14445/22315381/ijett-v29p260.

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48

Patel, Ketul, and Dr A. R. Patel. "Process of Web Usage Mining to find Interesting Patterns from Web Usage Data." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 3, no. 1 (August 1, 2012): 144–48. http://dx.doi.org/10.24297/ijct.v3i1c.2767.

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The traffic on World Wide Web is increasing rapidly and huge amount of data is generated due to users’ numerous interactions with web sites. Web Usage Mining is the application of data mining techniques to discover the useful and interesting patterns from web usage data. It supports to know frequently accessed pages, predict user navigation, improve web site structure etc. In order to apply Web Usage Mining, various steps are performed. This paper discusses the process of Web Usage Mining consisting steps: Data Collection, Pre-processing, Pattern Discovery and Pattern Analysis. It has also presented Web Usage Mining applications and some Web Mining software.
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49

He, Bo. "Personalized Web Data Mining System." Advanced Materials Research 219-220 (March 2011): 183–86. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.183.

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Most of Web data mining systems did not construct user profiles and could not support personalized Web data mining. Aiming at the shortcomings, the paper defined and established user profiles. On the base of this, the paper designed a personalized Web data mining system, namely PWDMS. PWDMS consisted of user interface module, data preprocessing module and data mining module. In addition, this paper discussed the key technology of PWDMS. It is proved that applying personalized technology to Web data mining is efficient.
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50

Malik, Varun, Vikas Rattan, Jaiteg Singh, Ruchi Mittal, and Urvashi Tandon. "Performance Comparison of Data Mining Classifiers on Web Log Data." Journal of Computational and Theoretical Nanoscience 17, no. 11 (November 1, 2020): 5113–16. http://dx.doi.org/10.1166/jctn.2020.9349.

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Web usage mining is the branch of web mining that deals with mining of data over the web. Web mining can be categorized as web content mining, web structure mining, web usage mining. In this paper, we have summarized the web usage mining results executed over the user tool WMOT (web mining optimized tool) based on the WEKA tool that has been used to apply various classification algorithms such as Naïve Bayes, KNN, SVM and tree based algorithms. Authors summarized the results of classification algorithms on WMOT tool and compared the results on the basis of classified instances and identify the algorithms that gives better instances accuracy.
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