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Journal articles on the topic 'WEB USAGE DATA'

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Published: 11 September 2023

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1

Thakur, Bhawesh Kumar, Syed Qamar Abbas, and Mohd Rizwan Beg. "Web Personalization Using Clustering of Web Usage Data." International Journal in Foundations of Computer Science & Technology 4, no. 5 (September 30, 2014): 69–84. http://dx.doi.org/10.5121/ijfcst.2014.4507.

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2

Garcia, Jorge Esparteiro, and Ana C. R. Paiva. "Maintaining Requirements Using Web Usage Data." Procedia Computer Science 100 (2016): 626–33. http://dx.doi.org/10.1016/j.procs.2016.09.204.

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3

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 usersâ€™ 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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Jarukasemratana, Sorn, and Tsuyoshi Murata. "Web Caching Replacement Algorithm Based on Web Usage Data." New Generation Computing 31, no. 4 (October 2013): 311–29. http://dx.doi.org/10.1007/s00354-013-0404-z.

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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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PADMAKUMAR, SUJATHA, Dr PUNITHAVALLI Dr.PUNITHAVALLI, and Dr RANJITH Dr.RANJITH. "A Web Usage Mining Approach to User Navigation Pattern and Prediction in Web Log Data." International Journal of Scientific Research 3, no. 4 (June 1, 2012): 92–94. http://dx.doi.org/10.15373/22778179/apr2014/34.

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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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8

Sandhyarani, Ramancha. "Construction of Community Web Directories based on Web usage Data." Advanced Computing: An International Journal 3, no. 2 (March 31, 2012): 41–48. http://dx.doi.org/10.5121/acij.2012.3205.

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9

Pierrakos, Dimitrios, and George Paliouras. "Personalizing Web Directories with the Aid of Web Usage Data." IEEE Transactions on Knowledge and Data Engineering 22, no. 9 (September 2010): 1331–44. http://dx.doi.org/10.1109/tkde.2009.173.

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Birukou, Aliaksandr, Enrico Blanzieri, Vincenzo DAndrea, Paolo Giorgini, and Natallia Kokash. "Improving Web Service Discovery with Usage Data." IEEE Software 24, no. 6 (November 2007): 47–54. http://dx.doi.org/10.1109/ms.2007.169.

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11

Berendt, Bettina, Laura Hollink, Vera Hollink, Markus Luczak-Rösch, Knud Möller, and David Vallet. "Usage analysis and the web of data." ACM SIGIR Forum 45, no. 1 (May 24, 2011): 63–69. http://dx.doi.org/10.1145/1988852.1988864.

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12

Munk, Michal, and Lubomir Benko. "Using Entropy in Web Usage Data Preprocessing." Entropy 20, no. 1 (January 22, 2018): 67. http://dx.doi.org/10.3390/e20010067.

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Tao, Yu-Hui, Tzung-Pei Hong, and Yu-Ming Su. "Web usage mining with intentional browsing data." Expert Systems with Applications 34, no. 3 (April 2008): 1893–904. http://dx.doi.org/10.1016/j.eswa.2007.02.017.

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14

DE, SUPRIYA KUMAR, and P. RADHA KRISHNA. "MINING WEB DATA USING CLUSTERING TECHNIQUE FOR WEB PERSONALIZATION." International Journal of Computational Intelligence and Applications 02, no. 03 (September 2002): 255–65. http://dx.doi.org/10.1142/s1469026802000580.

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Clustering of data in a large dimension space is of great interest in many data mining applications. In this paper, we propose a method for clustering of web usage data in a high-dimensional space based on a concept hierarchy model. In this method, the relationship present in the web usage data are mapped into a fuzzy proximity relation of user transactions. We also described an approach to present the preference set of URLs to a new user transaction based on the match score with the clusters. The study demonstrates that our approach is general and effective for mining the web data for web personalization.

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Rathipriya, R., K. Thangavel, and J. Bagyamani. "Usage Profile Generation from Web Usage Data Using Hybrid Biclustering Algorithm." International Journal of Applied Evolutionary Computation 2, no. 4 (October 2011): 37–49. http://dx.doi.org/10.4018/jaec.2011100103.

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Biclustering has the potential to make significant contributions in the fields of information retrieval, web mining, and so forth. In this paper, the authors analyze the complex association between users and pages of a web site by using a biclustering algorithm. This method automatically identifies the groups of users that show similar browsing patterns under a specific subset of the pages. In this paper, mutation operator from Genetic Algorithms is incorporated into the Binary Particle Swarm Optimization (BPSO) for biclustering of web usage data. This hybridization can increase the diversity of the population and help the particles effectively escape from the local optimum. It detects optimized user profile group according to coherent browsing behavior. Experiments are performed on a benchmark clickstream dataset to test the effectiveness of the proposed algorithm. The results show that the proposed algorithm has higher performance than existing PSO methods. The interpretation of this biclustering results are useful for marketing and sales strategies.

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Kartina Diah Kusuma Wardani. "Ekstraksi Click Stream Data Web E-Commerce Menggunakan Web Usage Mining." Jurnal Informatika Polinema 7, no. 2 (February 23, 2021): 65–72. http://dx.doi.org/10.33795/jip.v7i2.538.

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E-Commerce berkembang pesat dalam world wide web hingga menghasilkan berbagai jenis data yang dapat dianalisa lebih lanjut untuk berbagai keperluan seperti personifikasi web, profiling customer, dan sebagainya. Salah satu jenis data yang dihasilkan e-Commerce adalah click stream data web yang merekam aktivitas visitor web dalam bentuk log data selama berinteraksi pada laman web. Penelitian ini mengekstraksi click stream data web e-commerce untuk mendapatkan pola interaksi konsumen terhadap halaman web selama mengunjungi web e-commerce. Berdasarkan jenis data yang diekstrak maka web usage mining digunakan untuk ekstraksi pola dari click stream data yang berbentuk log data. Teknik mining yang dianalisa terhadap log data e-commerce pada penelitian ini terdiri dari frequent itemset, asociation rules, dan frequence sequence mining. Frequent itemset menghasilkan halaman web yang paling sering diakses oleh visitor. Association rules menghasilkan pola kemungkinan halaman web yang akan diakses visitor jika visitor mengakses halaman-halamn tertentu. Frequence sequence mining mendapatkan pola urutan halaman web yang paling sering diakses oleh visitor web e-commerce saat berinteraksi pada laman web. Pola urutan halaman yang diakses visitor menunjukkan urutan kebiasaan visitor mengunjungi e-commerce. Sedangkan teknik mining yang diimplementasikan untuk menghasilkan pola akses visitor pada penelitian ini adalah Frequence sequence mining. Hasil ekstraksi dari penelitian ini menunjukkan ada enam halaman web yang paling sering diakses oleh konsumen dengan berbagai pola urutan aksesnya.

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D., Ketan, and Satyen M. "Preprocessing on Web Server Log Data for Web Usage Pattern Discovery." International Journal of Computer Applications 165, no. 10 (May 17, 2017): 29–32. http://dx.doi.org/10.5120/ijca2017913978.

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V, Sathiyamoorthi, and Murali Bhaskaran .V. "DATA PREPARATION TECHNIQUES FOR WEB USAGE MINING IN WORLD WIDE WEB." International Journal on Information Sciences and Computing 4, no. 1 (2010): 55–60. http://dx.doi.org/10.18000/ijisac.50067.

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., R. Sandrilla, and M. Savitha Devi. "A Study on Data Preprocessing Methods on Web Log Data in Web Usage Mining." International Journal of Computer Sciences and Engineering 6, no. 7 (July 31, 2018): 920–28. http://dx.doi.org/10.26438/ijcse/v6i7.920928.

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20

Zhang, Xuejun, John Edwards, and Jenny Harding. "Personalised online sales using web usage data mining." Computers in Industry 58, no. 8-9 (December 2007): 772–82. http://dx.doi.org/10.1016/j.compind.2007.02.004.

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21

Tanasa, D., and B. Trousse. "Advanced data preprocessing for intersites Web usage mining." IEEE Intelligent Systems 19, no. 2 (March 2004): 59–65. http://dx.doi.org/10.1109/mis.2004.1274912.

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22

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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23

Ezzikouri, Hanane, Mohamed Fakir, Cherki Daoui, and Mohamed Erritali. "Extracting Knowledge from Web Data." Journal of Information Technology Research 7, no. 4 (October 2014): 27–41. http://dx.doi.org/10.4018/jitr.2014100103.

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The user behavior on a website triggers a sequence of queries that have a result which is the display of certain pages. The Information about these queries (including the names of the resources requested and responses from the Web server) are stored in a text file called a log file. Analysis of server log file can provide significant and useful information. Web Mining is the extraction of interesting and potentially useful patterns and implicit information from artifacts or activity related to the World Wide Web. Web usage mining is a main research area in Web mining focused on learning about Web users and their interactions with Web sites. The motive of mining is to find users' access models automatically and quickly from the vast Web log file, such as frequent access paths, frequent access page groups and user clustering. Through Web Usage Mining, several information left by user access can be mined which will provide foundation for decision making of organizations, Also the process of Web mining was defined as the set of techniques designed to explore, process and analyze large masses of consecutive information activities on the Internet, has three main steps: data preprocessing, extraction of reasons of the use and the interpretation of results. This paper will start with the presentation of different formats of web log files, then it will present the different preprocessing method that have been used, and finally it presents a system for “Web content and Usage Mining'' for web data extraction and web site analysis using Data Mining Algorithms Apriori, FPGrowth, K-Means, KNN, and ID3.

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Krishnaiah, N., and G. Narsimha. "Web Search Customization Approach Using Redundant Web Usage Data Association and Clustering." International Journal of Information Engineering and Electronic Business 8, no. 4 (July 8, 2016): 35–42. http://dx.doi.org/10.5815/ijieeb.2016.04.05.

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25

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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26

Huang, Joshua Z., Michael K. Ng, and Edmond H. Wu. "A data warehousing and data mining framework for web usage management." Communications in Information and Systems 4, no. 4 (2004): 301–24. http://dx.doi.org/10.4310/cis.2004.v4.n4.a3.

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27

Tao, Yu-Hui, Tzung-Pei Hong, Wen-Yang Lin, and Wen-Yuan Chiu. "A practical extension of web usage mining with intentional browsing data toward usage." Expert Systems with Applications 36, no. 2 (March 2009): 3937–45. http://dx.doi.org/10.1016/j.eswa.2008.02.058.

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Punjani, Murti. "A Survey on Data Preprocessing in Web Usage Mining." IOSR Journal of Computer Engineering 9, no. 4 (2013): 76–79. http://dx.doi.org/10.9790/0661-0947679.

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29

Rico, Mariano. "Simplifying Semantic Web application development and semantic data usage." AI Communications 23, no. 1 (2010): 65–66. http://dx.doi.org/10.3233/aic-2010-0472.

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30

Moktan, Gautam Raj, Nutti Varis, and Jukka Manner. "Utilizing Connection Usage Characteristics for Faster Web Data Transport." Journal of Computer Networks and Communications 2018 (June 6, 2018): 1–9. http://dx.doi.org/10.1155/2018/4520312.

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The quest for faster data transport to improve web user experience is ongoing and attempts are conducted from various fronts to realize it. On top of improving user experience, the implications of improving web data transport are also on the energy efficiency of wireless devices as well as user retention rates of service providers. HTTP/1.x allow the opening of multiple TCP connections per server and then using those connections for fetching multiple web objects through the use of HTTP pipelining. With the advent of HTTP/2.0, multiplexing is done inside a single connection to fetch multiple objects. In this paper, we analyze the TCP connections between the browser and the servers and examine their characteristics. We describe how an enhanced TCP variant can take advantage of data transport connection patterns. We show the benefits that enhanced TCP system can bring with the understanding of connection usage patterns. We find that such transport protocol can have effect in the page idle times as well as the connection concurrency during web page transfer. The results show significant improvement of page load times for both encryption heavy and unencrypted pages. We discuss the effect of the transport protocol on object transfer, connection duration, idle times during the page load, connections, and concurrency of flows that cumulate into page load times.

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Kaur, Jaswinder, and Kanwal Garg. "Discovery of Frequent Usage Pattern for Web Data to Optimized Web based Applications." International Journal of Computer Applications 145, no. 13 (July 15, 2016): 14–17. http://dx.doi.org/10.5120/ijca2016910865.

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Oktavianita, Annisa Dwi, Hendra Dea Arifin, Muhammad Dzulfikar Fauzi, and Aulia Faqih Rifa'i. "An Analysis of Memory Usage in Web Browser Software." IJID (International Journal on Informatics for Development) 5, no. 2 (December 26, 2016): 21. http://dx.doi.org/10.14421/ijid.2016.05204.

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A RAM or formerly known as a memory is a primary memory which helps swift data availability without waiting the whole data processed by the hard disk. A memory is also used by all installed applications including web browsers but there have been disappointed in cases of memory usages. Researchers use a descriptive quantitative approach with an observation, a central tendency and a dispersion method. There are 15 browsers chosen by random to be tested with low, medium and high loads to get their memory usage logs. Researchers proceed to analyze the log by using descriptive statistics to measure the central tendency and dispersion of data. A standard reference value from web application memory usage has been found as much as 393.38 MB. From that point, this research is successful and has been found the result. The web browser with the lowest memory usage is Flock with 134.67 MB and the web browser with the highest memory usage is Baidu with 699.66 MB.

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Abraham, Ajith. "Business Intelligence from Web Usage Mining." Journal of Information & Knowledge Management 02, no. 04 (December 2003): 375–90. http://dx.doi.org/10.1142/s0219649203000565.

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The rapid e-commerce growth has made both business community and customers face a new situation. Due to intense competition on the one hand and the customer's option to choose from several alternatives, the business community has realized the necessity of intelligent marketing strategies and relationship management. Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. Web usage mining has become very critical for effective Web site management, creating adaptive Web sites, business and support services, personalization, network traffic flow analysis and so on. This paper presents the important concepts of Web usage mining and its various practical applications. Further a novel approach called "intelligent-miner" (i-Miner) is presented. i-Miner could optimize the concurrent architecture of a fuzzy clustering algorithm (to discover web data clusters) and a fuzzy inference system to analyze the Web site visitor trends. A hybrid evolutionary fuzzy clustering algorithm is proposed to optimally segregate similar user interests. The clustered data is then used to analyze the trends using a Takagi-Sugeno fuzzy inference system learned using a combination of evolutionary algorithm and neural network learning. Proposed approach is compared with self-organizing maps (to discover patterns) and several function approximation techniques like neural networks, linear genetic programming and Takagi–Sugeno fuzzy inference system (to analyze the clusters). The results are graphically illustrated and the practical significance is discussed in detail. Empirical results clearly show that the proposed Web usage-mining framework is efficient.

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Saleh, Hillal, and Soukaena Hasheem. "A proposed strategy to secure web usage." Journal of Al-Rafidain University College For Sciences ( Print ISSN: 1681-6870 ,Online ISSN: 2790-2293 ), no. 1 (October 27, 2021): 19–27. http://dx.doi.org/10.55562/jrucs.v22i1.489.

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With much data on the web, it can be difficult, frustrating, and seemingly impossible to find the exact information you need. There are many powerful search utilities on the web are called search engines, in addition to the visitor tracking in a web to study exactly the behavior of the web visitors, to improve the efficiency of that web. This research concentrates on a particular aspect, which is applying Data mining technique especially by association analysis algorithm on the encrypted web log files, that to ensure the privacy of the original data for these files. So since the input data introduced to mining algorithm is encrypted then the resulted association rules are encrypted that to ensure the privacy of the extracted knowledge. Then analyze the decrypted web log data file for the web usage, to study the visitor tracking. According to this study the server configurations and all the services will be improved.

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35

Jawale, Sakshi, Pranit Londhe, Rushikesh Kolekar, Sarika Jadhav, and Prajwali Kadam. "Data Summarization Web Application." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (May 31, 2023): 883–95. http://dx.doi.org/10.22214/ijraset.2023.51650.

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Abstract: As there is an increase in the usage of digital applications, the availability of data generated has increased to a tremendous scale. Data is an important component in almost every domain where research and analysis are required to solve problems. It is available in a structured or unstructured format. Therefore, to get corresponding data as per the application purpose, easily and quickly from different sources of data on the internet, an online content summarizer is desired. Summarizers make it easier for users to understand the content without reading it completely. Abstractive Text Summarizer helps in defining the content by considering the important words and helps in creating summaries that are in a human-readable format. The main aim is to make summaries in such a way that they should not lose their context. Various Neural Network models are employed along with other machine translation models to bring about a concise summary generation. This paper aims to highlight and study the existing contemporary models for abstractive text summarization and also to explore areas for further research

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36

HU, JIA, and NING ZHONG. "WEB FARMING WITH CLICKSTREAM." International Journal of Information Technology & Decision Making 07, no. 02 (June 2008): 291–308. http://dx.doi.org/10.1142/s0219622008002971.

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In a commercial website or portal, Web information fusion is usually from the following two approaches, one is to integrate the Web content, structure, and usage data for surfing behavior analysis; the other is to integrate Web usage data with traditional customer, product, and transaction data for purchasing behavior analysis. In this paper, we propose a unified model based on Web farming technology for collecting clickstream logs in the whole user interaction process. We emphasize that collecting clickstream logs at the application layer will help to seamlessly integrate Web usage data with other customer-related data sources. In this paper, we extend the Web log standard to modeling clickstream format and Web mining to Web farming from passively collecting data and analyzing the customer behavior to actively influence the customer's decision making. The proposed model can be developed as a common plugin for most existing commercial websites and portals.

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37

Kapusta, Jozef, Michal Munk, Dominik Halvoník, and Martin Drlík. "User Identification in the Process of Web Usage Data Preprocessing." International Journal of Emerging Technologies in Learning (iJET) 14, no. 09 (May 14, 2019): 21. http://dx.doi.org/10.3991/ijet.v14i09.9854.

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If we are talking about user behavior analytics, we have to understand what the main source of valuable information is. One of these sources is definitely a web server. There are multiple places where we can extract the necessary data. The most common ways are to search for these data in access log, error log, custom log files of web server, proxy server log file, web browser log, browser cookies etc. A web server log is in its default form known as a Common Log File (W3C, 1995) and keeps information about IP address; date and time of visit; ac-cessed and referenced resource. There are standardized methodologies which contain several steps leading to extract new knowledge from provided data. Usu-ally, the first step is in each one of them to identify users, users’ sessions, page views, and clickstreams. This process is called pre-processing. Main goal of this stage is to receive unprocessed web server log file as input and after processing outputs meaningful representations which can be used in next phase. In this pa-per, we describe in detail user session identification which can be considered as most important part of data pre-processing. Our paper aims to compare the us-er/session identification using the STT with the identification of user/session us-ing cookies. This comparison was performed concerning the quality of the se-quential rules generated, i.e., a comparison was made regarding generation useful, trivial and inexplicable rules.

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G., Shivaprasad, N. V. Subba Reddy, and U. Dinesh Acharya. "Knowledge Discovery from Web Usage Data: An Efficient Implementation of Web Log Preprocessing Techniques." International Journal of Computer Applications 111, no. 13 (February 18, 2015): 27–32. http://dx.doi.org/10.5120/19600-1451.

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39

Rao, Kannasani Srinivasa, M. Krishnamurthy, and A. Kannan. "Extracting the User’s Interests by Using Web Log Data Based on Web Usage Mining." Journal of Computational and Theoretical Nanoscience 12, no. 12 (December 1, 2015): 5031–40. http://dx.doi.org/10.1166/jctn.2015.4468.

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40

Ansari, Zahid, Syed Abdul Sattar, A. Vinaya Babu, and M. Fazle Azeem. "Mountain density-based fuzzy approach for discovering web usage clusters from web log data." Fuzzy Sets and Systems 279 (November 2015): 40–63. http://dx.doi.org/10.1016/j.fss.2015.01.021.

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41

Santhisree, Ms K., and Dr A. Damodaram. "Clustering on Web usage data using Approximations and Set Similarities." International Journal of Computer Applications 1, no. 4 (February 25, 2010): 27–31. http://dx.doi.org/10.5120/107-218.

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42

Alam, Shafiq, Gillian Dobbie, Yun Sing Koh, and Patricia Riddle. "Web usage mining based recommender systems using implicit heterogeneous data." Web Intelligence and Agent Systems: An International Journal 12, no. 4 (2014): 389–409. http://dx.doi.org/10.3233/wia-140302.

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43

Mitrovic, Zoran, and Zita Bošnjak. "Discovering Interesting Association Rules in the Web Log Usage Data." Interdisciplinary Journal of Information, Knowledge, and Management 5 (2010): 191–207. http://dx.doi.org/10.28945/1159.

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Prasad, G. V. S. CH S. L. V., Malapati Sri Rama Lakshmi Reddy, Kuntam Babu Rao, and Chodagam Suresh Kumar. "Approach for Developing Business Statistics Using Data Web Usage Mining." International Journal Of Recent Advances in Engineering & Technology 08, no. 02 (February 29, 2020): 1–3. http://dx.doi.org/10.46564/ijraet.2020.v08i02.001.

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Kwakye, Michael Mireku. "Privacy-preservation in data pre-processing for web usage mining." International Journal of Information Privacy, Security and Integrity 4, no. 2 (2019): 134. http://dx.doi.org/10.1504/ijipsi.2019.10028212.

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Kwakye, Michael Mireku. "Privacy-preservation in data pre-processing for web usage mining." International Journal of Information Privacy, Security and Integrity 4, no. 2 (2019): 134. http://dx.doi.org/10.1504/ijipsi.2019.106605.

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Rathipriya, R., K. Thangavel, and J. Bagyamani. "Binary Particle Swarm Optimization based Biclustering of Web Usage Data." International Journal of Computer Applications 25, no. 2 (July 31, 2011): 43–49. http://dx.doi.org/10.5120/3001-4036.

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Kazeminuri, G., A. Harounabadi, and J. Mirabedini. "Web Personalization With Web Usage Mining Technics and Association Rules." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 15, no. 1 (October 2, 2015): 6394–401. http://dx.doi.org/10.24297/ijct.v15i1.1711.

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As amount of information and web development increase considerably, some technics and methods are required to allow efficient access to data and information extraction from them. Extracting useful pattern from worldwide networks that are referred to as web mining is considered as one of the main applications of data mining. The key challenges of web users are exploring websites for finding the relevant information by taking minimum time in an efficient manner. Discovering the hidden knowledge in the manner of interaction in the web is considered as one of the most important technics in web utilization mining. Information overload is one of the main problems in current web and for tackling this problem the web personalization systems are presented that adapts the content and services of a website with user's interests and browsing behavior. Today website personalization is turned into a popular event for web users and it plays a leading role in speed of access and providing users' desirable information. The objective of current article is extracting index based on users' behavior and web personalization using web mining technics based on utilization and association rules. In proposed methods the weighting criteria showing the extent of interest of users to the pages are expressed and a method is presented based on combination of association rules and clustering by perceptron neural network for web personalization. The proposed method simulation results suggest the improvement of precision and coverage criteria with respect to other compared methods.

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Liu, Wen Tao. "Web Page Data Collection Based on Multithread." Applied Mechanics and Materials 347-350 (August 2013): 2575–79. http://dx.doi.org/10.4028/www.scientific.net/amm.347-350.2575.

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The web data collection is the process of collecting the semi-structured, large-scale and redundant data which include web content, web structure and web usage in the web by the crawler and it is often used for the information extraction, information retrieval, search engine and web data mining. In this paper, the web data collection principle is introduced and some related topics are discussed such as page download, coding problem, updated strategy, static and dynamic page. The multithread technology is described and multithread mode for the web data collection is proposed. The web data collection with multithread can get better resource utilization, better average response time and better performance.

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Ridho, Farid, and Fachruddin Mansyur. "ANALISIS POLA PERMINTAAN PUBLIKASI DATA BADAN PUSAT STATISTIK MENGGUNAKAN ASSOCIATION RULE APRIORI." KLIK - KUMPULAN JURNAL ILMU KOMPUTER 7, no. 2 (June 28, 2020): 187. http://dx.doi.org/10.20527/klik.v7i2.322.

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<p><em>BPS is a data provider body in Indonesia. In publishing, BPS uses a variety of media, one of which is the BPS website. To get data through the BPS website, users can visit the website then download the data they need. The services obtained by data users on the BPS website depend on the quality of the website. The better the quality, the better the service experience gained by data users. The method that can be used to improve the quality of a website is the web usage mining method. Web usage mining is the application of data mining techniques on web repositories to study usage patterns. The purpose of this study is to determine the pattern of data publication requests on the BPS website which can later be used as a reference to improve the quality of BPS website services. Based on the results of the study, it was found that data users tend to access the same data with different years simultaneously. For results by grouping data by title without year, obtained quite diverse rules.</em></p><p><em><strong>Keywords</strong></em><em>: </em><em>web usage mining, association rule, apriori</em></p><p><em>BPS merupakan badan penyedia data di Indonesia. Dalam mempublikasikan datanya, BPS menggunakan berbagai media, salah satunya adalah website BPS. Untuk mendapatkan data melalui website BPS, pengguna dapat mengunjungi website kemudian mengunduh data yang mereka butuhkan. Layanan yang didapatkan oleh pengguna data pada website BPS tergantung dari kualitas website tersebut. Semakin baik kualitasnya, semakin baik pula pengalaman pelayanan yang didapatkan oleh pengguna data. Metode yang dapat digunakan untuk meningkatkan kualitas suatu website adalah metode web usage mining. Web usage mining merupakan penerapan tekhnik data mining pada web repositori untuk mempelajari pola penggunaan</em><em>. </em><em>Tujuan dari penelitian ini adalah untuk mengetahui pola permintaan publikasi data pada website BPS yang nantinya dapat digunakan sebagai acuan untuk meningkatkan kualitas layanan website BPS. Berdasarkan hasil penelitian, didapatkan bahwa pengguna data cenderung mengakses data yang sama dengan tahun yang berbeda secara bersamaan. Untuk hasil dengan mengelompokan data berdasarkan judul tanpa tahun, diperoleh rules yang cukup beragam.</em></p><p><em><strong>Kata kunci</strong></em><em>: </em><em>web usage mining, association rule, apriori</em></p>

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