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Academic literature on the topic 'Web usage data mining techniques'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Web usage data mining techniques"

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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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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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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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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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HOGO, MOFREH, MIROSLAV SNOREK, and PAWAN LINGRAS. "TEMPORAL VERSUS LATEST SNAPSHOT WEB USAGE MINING USING KOHONEN SOM AND MODIFIED KOHONEN SOM BASED ON THE PROPERTIES OF ROUGH SETS THEORY." International Journal on Artificial Intelligence Tools 13, no. 03 (September 2004): 569–91. http://dx.doi.org/10.1142/s0218213004001697.

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Temporal Web usage mining involves application of data mining techniques on temporal Web usage data to discover temporal usage patterns, which describe the temporal behavior of users on the Internet Web site, to understand the temporal users' behavior during different time slices. Clustering and classification are two important functions in Web mining. Classes, and associations in Web mining do not necessarily have crisp boundaries. Therefore the conventional clustering techniques became unsuitable to find such clusters and associations, where these conventional classification algorithms provide crisp classes, which are not suitable in real world applications. This gives the chance of using the non-conventional clustering techniques as fuzzy and rough sets in Web mining clustering applications. Recent research introduced the adaptation of Kohonen SOM based on the properties of rough sets theory to find the interval set clusters for the users on the Internet. This paper introduces the comparison between the latest snapshot Web usage mining and the temporal Web usage mining, and. the comparison between the temporal Web usage mining using the conventional Kohonen SOM and the modified Kohonen SOM based on the properties of sets theory.
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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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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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Ramanathaiah, Ramakrishnan M., Bhawna Nigam, and M. Niranjanamurthy. "Construction of User’s Navigation Sessions from Web Logs for Web Usage Mining." Journal of Computational and Theoretical Nanoscience 17, no. 9 (July 1, 2020): 4432–37. http://dx.doi.org/10.1166/jctn.2020.9091.

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Web Usage Mining applies fewer techniques in record data to pull out the behavior of users. The knowledge mined from the web log can be utilized in web personalization, Prediction, prefetching, restructuring of web sites etc. It consists of three steps in preprocessing, pattern detection and analysis. Web log information is typically noisy and uncertain and preprocessing is a significant process ahead of mining. The Patterns discovered after applying the mining techniques are dependent on the accuracy of the weblog which in turn depends on the preprocessing phase. The output of preprocessing should be the user’s navigation session file. In this paper the techniques of preprocessing and the method for construction of user’s navigation session file is proposed.
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Hamodi, Yaser Issam, Ruaa Riyadh Hussein, and Naeem Th Yousir. "Development of a Unifying Theory for Data Mining Using Clustering Techniques." Webology 17, no. 2 (December 21, 2020): 01–14. http://dx.doi.org/10.14704/web/v17i2/web17012.

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A performance evaluation of four different clustering techniques was carried out based on segmenting consumer by product type and by product usage in the research. Cobweb, DBSCAN, EM and k-means algorithms were evaluated based on the computational time, accuracy of the result produced and the purity of the result produced. The experiment was performed using WEKA as a data mining tool. The performance evaluation of the four techniques showed that K-means outperformed others in all considered evaluation measure while the EM technique was the second best in terms of accuracy and purity, outperforming the other two. DBSCAN technique was the 3rd best of the selected algorithms even as its computational time is shorter than that of EM while the fourth best performing calculation has been believed to be the Spider web calculation as respects to immaculateness, exactness and computational time.
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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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Dissertations / Theses on the topic "Web usage data mining techniques"

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Khalil, Faten. "Combining web data mining techniques for web page access prediction." University of Southern Queensland, Faculty of Sciences, 2008. http://eprints.usq.edu.au/archive/00004341/.

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[Abstract]: Web page access prediction gained its importance from the ever increasing number of e-commerce Web information systems and e-businesses. Web page prediction, that involves personalising the Web users’ browsing experiences, assists Web masters in the improvement of the Web site structure and helps Web users in navigating the site and accessing the information they need. The most widely used approach for this purpose is the pattern discovery process of Web usage mining that entails many techniques like Markov model, association rules and clustering. Implementing pattern discovery techniques as such helps predict the next page tobe accessed by theWeb user based on the user’s previous browsing patterns. However, each of the aforementioned techniques has its own limitations, especiallywhen it comes to accuracy and space complexity. This dissertation achieves better accuracy as well as less state space complexity and rules generated by performingthe following combinations. First, we combine low-order Markov model and association rules. Markov model analysis are performed on the data sets. If the Markov model prediction results in a tie or no state, association rules are used for prediction. The outcome of this integration is better accuracy, less Markov model state space complexity and less number of generated rules than using each of the methods individually. Second, we integrate low-order Markov model and clustering. The data sets are clustered and Markov model analysis are performed oneach cluster instead of the whole data sets. The outcome of the integration is better accuracy than the first combination with less state space complexity than higherorder Markov model. The last integration model involves combining all three techniques together: clustering, association rules and low-order Markov model. The data sets are clustered and Markov model analysis are performed on each cluster. If the Markov model prediction results in close accuracies for the same item, association rules are used for prediction. This integration model achievesbetter Web page access prediction accuracy, less Markov model state space complexity and less number of rules generated than the previous two models.
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Nagi, Mohamad. "Integrating Network Analysis and Data Mining Techniques into Effective Framework for Web Mining and Recommendation. A Framework for Web Mining and Recommendation." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/14200.

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The main motivation for the study described in this dissertation is to benefit from the development in technology and the huge amount of available data which can be easily captured, stored and maintained electronically. We concentrate on Web usage (i.e., log) mining and Web structure mining. Analysing Web log data will reveal valuable feedback reflecting how effective the current structure of a web site is and to help the owner of a web site in understanding the behaviour of the web site visitors. We developed a framework that integrates statistical analysis, frequent pattern mining, clustering, classification and network construction and analysis. We concentrated on the statistical data related to the visitors and how they surf and pass through the various pages of a given web site to land at some target pages. Further, the frequent pattern mining technique was used to study the relationship between the various pages constituting a given web site. Clustering is used to study the similarity of users and pages. Classification suggests a target class for a given new entity by comparing the characteristics of the new entity to those of the known classes. Network construction and analysis is also employed to identify and investigate the links between the various pages constituting a Web site by constructing a network based on the frequency of access to the Web pages such that pages get linked in the network if they are identified in the result of the frequent pattern mining process as frequently accessed together. The knowledge discovered by analysing a web site and its related data should be considered valuable for online shoppers and commercial web site owners. Benefitting from the outcome of the study, a recommendation system was developed to suggest pages to visitors based on their profiles as compared to similar profiles of other visitors. The conducted experiments using popular datasets demonstrate the applicability and effectiveness of the proposed framework for Web mining and recommendation. As a by product of the proposed method, we demonstrate how it is effective in another domain for feature reduction by concentrating on gene expression data analysis as an application with some interesting results reported in Chapter 5.
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Khasawneh, Natheer Yousef. "Toward Better Website Usage: Leveraging Data Mining Techniques and Rough Set Learning to Construct Better-to-use Websites." Akron, OH : University of Akron, 2005. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=akron1120534472.

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Dissertation (Ph. D.)--University of Akron, Dept. of Electrical and Computer Engineering, 2005.
"August, 2005." Title from electronic dissertation title page (viewed 01/14/2006) Advisor, John Durkin; Committee members, John Welch, James Grover, Yueh-Jaw Lin, Yingcai Xiao, Chien-Chung Chan; Department Chair, Alex Jose De Abreu-Garcia; Dean of the College, George Haritos; Dean of the Graduate School, George R. Newkome. Includes bibliographical references.
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Ammari, Ahmad N. "Transforming user data into user value by novel mining techniques for extraction of web content, structure and usage patterns : the development and evaluation of new Web mining methods that enhance information retrieval and improve the understanding of users' Web behavior in websites and social blogs." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5269.

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The rapid growth of the World Wide Web in the last decade makes it the largest publicly accessible data source in the world, which has become one of the most significant and influential information revolution of modern times. The influence of the Web has impacted almost every aspect of humans' life, activities and fields, causing paradigm shifts and transformational changes in business, governance, and education. Moreover, the rapid evolution of Web 2.0 and the Social Web in the past few years, such as social blogs and friendship networking sites, has dramatically transformed the Web from a raw environment for information consumption to a dynamic and rich platform for information production and sharing worldwide. However, this growth and transformation of the Web has resulted in an uncontrollable explosion and abundance of the textual contents, creating a serious challenge for any user to find and retrieve the relevant information that he truly seeks to find on the Web. The process of finding a relevant Web page in a website easily and efficiently has become very difficult to achieve. This has created many challenges for researchers to develop new mining techniques in order to improve the user experience on the Web, as well as for organizations to understand the true informational interests and needs of their customers in order to improve their targeted services accordingly by providing the products, services and information that truly match the requirements of every online customer. With these challenges in mind, Web mining aims to extract hidden patterns and discover useful knowledge from Web page contents, Web hyperlinks, and Web usage logs. Based on the primary kinds of Web data used in the mining process, Web mining tasks can be categorized into three main types: Web content mining, which extracts knowledge from Web page contents using text mining techniques, Web structure mining, which extracts patterns from the hyperlinks that represent the structure of the website, and Web usage mining, which mines user's Web navigational patterns from Web server logs that record the Web page access made by every user, representing the interactional activities between the users and the Web pages in a website. The main goal of this thesis is to contribute toward addressing the challenges that have been resulted from the information explosion and overload on the Web, by proposing and developing novel Web mining-based approaches. Toward achieving this goal, the thesis presents, analyzes, and evaluates three major contributions. First, the development of an integrated Web structure and usage mining approach that recommends a collection of hyperlinks for the surfers of a website to be placed at the homepage of that website. Second, the development of an integrated Web content and usage mining approach to improve the understanding of the user's Web behavior and discover the user group interests in a website. Third, the development of a supervised classification model based on recent Social Web concepts, such as Tag Clouds, in order to improve the retrieval of relevant articles and posts from Web social blogs.
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Ammari, Ahmad N. "Transforming user data into user value by novel mining techniques for extraction of web content, structure and usage patterns. The Development and Evaluation of New Web Mining Methods that enhance Information Retrieval and improve the Understanding of User¿s Web Behavior in Websites and Social Blogs." Thesis, University of Bradford, 2010. http://hdl.handle.net/10454/5269.

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The rapid growth of the World Wide Web in the last decade makes it the largest publicly accessible data source in the world, which has become one of the most significant and influential information revolution of modern times. The influence of the Web has impacted almost every aspect of humans' life, activities and fields, causing paradigm shifts and transformational changes in business, governance, and education. Moreover, the rapid evolution of Web 2.0 and the Social Web in the past few years, such as social blogs and friendship networking sites, has dramatically transformed the Web from a raw environment for information consumption to a dynamic and rich platform for information production and sharing worldwide. However, this growth and transformation of the Web has resulted in an uncontrollable explosion and abundance of the textual contents, creating a serious challenge for any user to find and retrieve the relevant information that he truly seeks to find on the Web. The process of finding a relevant Web page in a website easily and efficiently has become very difficult to achieve. This has created many challenges for researchers to develop new mining techniques in order to improve the user experience on the Web, as well as for organizations to understand the true informational interests and needs of their customers in order to improve their targeted services accordingly by providing the products, services and information that truly match the requirements of every online customer. With these challenges in mind, Web mining aims to extract hidden patterns and discover useful knowledge from Web page contents, Web hyperlinks, and Web usage logs. Based on the primary kinds of Web data used in the mining process, Web mining tasks can be categorized into three main types: Web content mining, which extracts knowledge from Web page contents using text mining techniques, Web structure mining, which extracts patterns from the hyperlinks that represent the structure of the website, and Web usage mining, which mines user's Web navigational patterns from Web server logs that record the Web page access made by every user, representing the interactional activities between the users and the Web pages in a website. The main goal of this thesis is to contribute toward addressing the challenges that have been resulted from the information explosion and overload on the Web, by proposing and developing novel Web mining-based approaches. Toward achieving this goal, the thesis presents, analyzes, and evaluates three major contributions. First, the development of an integrated Web structure and usage mining approach that recommends a collection of hyperlinks for the surfers of a website to be placed at the homepage of that website. Second, the development of an integrated Web content and usage mining approach to improve the understanding of the user's Web behavior and discover the user group interests in a website. Third, the development of a supervised classification model based on recent Social Web concepts, such as Tag Clouds, in order to improve the retrieval of relevant articles and posts from Web social blogs.
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Norguet, Jean-Pierre. "Semantic analysis in web usage mining." Doctoral thesis, Universite Libre de Bruxelles, 2006. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210890.

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With the emergence of the Internet and of the World Wide Web, the Web site has become a key communication channel in organizations. To satisfy the objectives of the Web site and of its target audience, adapting the Web site content to the users' expectations has become a major concern. In this context, Web usage mining, a relatively new research area, and Web analytics, a part of Web usage mining that has most emerged in the corporate world, offer many Web communication analysis techniques. These techniques include prediction of the user's behaviour within the site, comparison between expected and actual Web site usage, adjustment of the Web site with respect to the users' interests, and mining and analyzing Web usage data to discover interesting metrics and usage patterns. However, Web usage mining and Web analytics suffer from significant drawbacks when it comes to support the decision-making process at the higher levels in the organization.

Indeed, according to organizations theory, the higher levels in the organizations need summarized and conceptual information to take fast, high-level, and effective decisions. For Web sites, these levels include the organization managers and the Web site chief editors. At these levels, the results produced by Web analytics tools are mostly useless. Indeed, most of these results target Web designers and Web developers. Summary reports like the number of visitors and the number of page views can be of some interest to the organization manager but these results are poor. Finally, page-group and directory hits give the Web site chief editor conceptual results, but these are limited by several problems like page synonymy (several pages contain the same topic), page polysemy (a page contains several topics), page temporality, and page volatility.

Web usage mining research projects on their part have mostly left aside Web analytics and its limitations and have focused on other research paths. Examples of these paths are usage pattern analysis, personalization, system improvement, site structure modification, marketing business intelligence, and usage characterization. A potential contribution to Web analytics can be found in research about reverse clustering analysis, a technique based on self-organizing feature maps. This technique integrates Web usage mining and Web content mining in order to rank the Web site pages according to an original popularity score. However, the algorithm is not scalable and does not answer the page-polysemy, page-synonymy, page-temporality, and page-volatility problems. As a consequence, these approaches fail at delivering summarized and conceptual results.

An interesting attempt to obtain such results has been the Information Scent algorithm, which produces a list of term vectors representing the visitors' needs. These vectors provide a semantic representation of the visitors' needs and can be easily interpreted. Unfortunately, the results suffer from term polysemy and term synonymy, are visit-centric rather than site-centric, and are not scalable to produce. Finally, according to a recent survey, no Web usage mining research project has proposed a satisfying solution to provide site-wide summarized and conceptual audience metrics.

In this dissertation, we present our solution to answer the need for summarized and conceptual audience metrics in Web analytics. We first described several methods for mining the Web pages output by Web servers. These methods include content journaling, script parsing, server monitoring, network monitoring, and client-side mining. These techniques can be used alone or in combination to mine the Web pages output by any Web site. Then, the occurrences of taxonomy terms in these pages can be aggregated to provide concept-based audience metrics. To evaluate the results, we implement a prototype and run a number of test cases with real Web sites.

According to the first experiments with our prototype and SQL Server OLAP Analysis Service, concept-based metrics prove extremely summarized and much more intuitive than page-based metrics. As a consequence, concept-based metrics can be exploited at higher levels in the organization. For example, organization managers can redefine the organization strategy according to the visitors' interests. Concept-based metrics also give an intuitive view of the messages delivered through the Web site and allow to adapt the Web site communication to the organization objectives. The Web site chief editor on his part can interpret the metrics to redefine the publishing orders and redefine the sub-editors' writing tasks. As decisions at higher levels in the organization should be more effective, concept-based metrics should significantly contribute to Web usage mining and Web analytics.


Doctorat en sciences appliquées
info:eu-repo/semantics/nonPublished

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Sobolewska, Katarzyna-Ewa. "Web links utility assessment using data mining techniques." Thesis, Blekinge Tekniska Högskola, Avdelningen för programvarusystem, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2936.

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This paper is focusing on the data mining solutions for the WWW, specifically how it can be used for the hyperlinks evaluation. We are focusing on the hyperlinks used in the web sites systems and on the problem which consider evaluation of its utility. Since hyperlinks reflect relation to other webpage one can expect that there exist way to verify if users follow desired navigation paths. The Challenge is to use available techniques to discover usage behavior patterns and interpret them. We have evaluated hyperlinks of the selected pages from www.bth.se web site. By using web expert’s help the usefulness of the data mining as the assessment basis was validated. The outcome of the research shows that data mining gives decision support for the changes in the web site navigational structure.
akasha.kate@gmail.com
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Bayir, Murat Ali. "A New Reactive Method For Processing Web Usage Data." Master's thesis, METU, 2007. http://etd.lib.metu.edu.tr/upload/12607323/index.pdf.

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In this thesis, a new reactive session reconstruction method '
Smart-SRA'
is introduced. Web usage mining is a type of web mining, which exploits data mining techniques to discover valuable information from navigations of Web users. As in classical data mining, data processing and pattern discovery are the main issues in web usage mining. The first phase of the web usage mining is the data processing phase including session reconstruction. Session reconstruction is the most important task of web usage mining since it directly affects the quality of the extracted frequent patterns at the final step, significantly. Session reconstruction methods can be classified into two categories, namely '
reactive'
and '
proactive'
with respect to the data source and the data processing time. If the user requests are processed after the server handles them, this technique is called as &lsquo
reactive&rsquo
, while in &lsquo
proactive&rsquo
strategies this processing occurs during the interactive browsing of the web site. Smart-SRA is a reactive session reconstruction techique, which uses web log data and the site topology. In order to compare Smart-SRA with previous reactive methods, a web agent simulator has been developed. Our agent simulator models behavior of web users and generates web user navigations as well as the log data kept by the web server. In this way, the actual user sessions will be known and the successes of different techniques can be compared. In this thesis, it is shown that the sessions generated by Smart-SRA are more accurate than the sessions constructed by previous heuristics.
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Wu, Hao-cun, and 吳浩存. "A multidimensional data model for monitoring web usage and optimizing website topology." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2004. http://hub.hku.hk/bib/B29528215.

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Özakar, Belgin Püskülcü Halis. "Finding And Evaluating Patterns In Wes Repository Using Database Technology And Data Mining Algorithms/." [s.l.]: [s.n.], 2002. http://library.iyte.edu.tr/tezler/master/bilgisayaryazilimi/T000130.pdf.

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Books on the topic "Web usage data mining techniques"

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Web data mining: Exploring hyperlinks, contents, and usage data. 2nd ed. Heidelberg: Springer, 2011.

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author, Roghani Ali, ed. Big data analytics for beginners. [India]: Crux Tech Limited, 2014.

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Taniar, David, and Lukman Hakim Iwan. Exploring advances in interdisciplinary data mining and analytics: New trends. Hershey, PA: Information Science Reference, 2012.

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Dēta mainingu to shūgōchi: Kiso kara web, sōsharu media made = Data mining and collective intelligence from basics to web and social media. Tōkyō: Kyōritsu Shuppan, 2012.

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do, Prado Hercules Antonio, and Ferneda Edilson, eds. Emerging technologies of text mining: Techniques and applications. Hershey PA: Information Science Reference, 2007.

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service), ScienceDirect (Online, ed. Cult of analytics: Driving online marketing strategies using Web analytics. Amsterdam: Elsevier/Butterworth-Heinemann, 2009.

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Zaïane, Osmar R., Jaideep Srivastava, Myra Spiliopoulou, and Brij Masand, eds. WEBKDD 2002 - Mining Web Data for Discovering Usage Patterns and Profiles. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/b11784.

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Abraham, Kandel, ed. Search engines, link analysis, and user's Web behavior: [a unifying Web mining approach]. Berlin: Springer, 2008.

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1968-, Nasraoui Olfa, ed. Advances in web mining and web usage analysis: 8th International Workshop on Knowledge Discovery on the Web, WebKDD 2006, Philadelphia, PA, USA, August 20, 2006 : revised papers. Berlin: Springer, 2007.

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Developments in data extraction, management, and analysis. Hershey, PA: Information Science Reference, 2012.

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Book chapters on the topic "Web usage data mining techniques"

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Aggarwal, Charu C., and Philip S. Yu. "On Clustering Techniques for Change Diagnosis in Data Streams." In Advances in Web Mining and Web Usage Analysis, 139–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11891321_8.

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de Paiva, Fábio A. Procópio, and José Alfredo F. Costa. "Using SOM to Clustering of Web Sessions Extracted by Techniques of Web Usage Mining." In Intelligent Data Engineering and Automated Learning - IDEAL 2012, 484–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32639-4_59.

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Liu, Bing, Bamshad Mobasher, and Olfa Nasraoui. "Web Usage Mining." In Web Data Mining, 527–603. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19460-3_12.

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Román, Pablo E., Gastón L’Huillier, and Juan D. Velásquez. "Web Usage Mining." In Advanced Techniques in Web Intelligence - I, 143–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14461-5_6.

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Castellano, Giovanna, Anna M. Fanelli, and Maria A. Torsello. "Web Usage Mining: Discovering Usage Patterns for Web Applications." In Advanced Techniques in Web Intelligence-2, 75–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33326-2_4.

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da Silva, Alzennyr, Yves Lechevallier, Fabrice Rossi, and Francisco de Carvalho. "Clustering Dynamic Web Usage Data." In Innovative Applications in Data Mining, 71–82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88045-5_4.

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Rossi, Fabrice, Francisco De Carvalho, Yves Lechevallier, and Alzennyr Da Silva. "Dissimilarities for Web Usage Mining." In Studies in Classification, Data Analysis, and Knowledge Organization, 39–46. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/3-540-34416-0_5.

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Saidi, Samia, and Yahya Slimani. "Enhancing Web Caching Using Web Usage Mining Techniques." In Communications in Computer and Information Science, 425–35. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14171-3_38.

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L’Huillier, Gaston, and Juan D. Velásquez. "Web Usage Data Pre-processing." In Advanced Techniques in Web Intelligence-2, 11–34. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-33326-2_2.

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Li, Xiang-ying. "Data Preprocessing in Web Usage Mining." In The 19th International Conference on Industrial Engineering and Engineering Management, 257–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38391-5_27.

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Conference papers on the topic "Web usage data mining techniques"

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Nassar, Omer Adel, and Nedhal A. Al Saiyd. "The integrating between web usage mining and data mining techniques." In 2013 5th International Conference on Computer Science and Information Technology (CSIT). IEEE, 2013. http://dx.doi.org/10.1109/csit.2013.6588787.

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Sukumar, P., L. Robert, and S. Yuvaraj. "Review on modern Data Preprocessing techniques in Web usage mining (WUM)." In 2016 International Conference on Computation System and Information Technology for Sustainable Solutions (CSITSS). IEEE, 2016. http://dx.doi.org/10.1109/csitss.2016.7779441.

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Liu, Fei. "Research on data preparation technique in web usage mining." In 2013 International Conference of Information Technology and Industrial Engineering. Southampton, UK: WIT Press, 2013. http://dx.doi.org/10.2495/itie131492.

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Braz, Fernando J. "Knowledge Discovery on Trajectory Data Warehouses: Possible usage of the Data Mining Techniques." In Simpósio Brasileiro de Sistemas de Informação. Sociedade Brasileira de Computação, 2008. http://dx.doi.org/10.5753/sbsi.2008.5921.

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In this paper we are interested in discussing the possibility of theusage of Data Mining tasks in order to reveal knowledge resident in Trajectory Data Warehouses (TDW). We consider a data stream environment where a set of mobile objects send the data about its location in a irregular and unbounded way. The data volume is stored in a centralized and traditional DW with precomputed aggregations values (preserving the trajectories privacy). Through of analysis of the TDW measures (pre-computed aggregation values) we can reveal some characteristics about trajectories in a given spatio-temporal area. The revealed knowledge can be useful in order to describe or show the occurrence of a real phenomenon. We present a review of a proposed structure of a TDW and discuss the use of Data Mining tasks to improve the analysis of the trajectory data warehouse environment.
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Shivaprasad, G., N. V. Subbareddy, U. Dinesh Acharya, R. B. Patel, and B. P. Singh. "Knowledge Discovery from Web Usage Data: Research and Development of Web Access Pattern Tree Based Sequential Pattern Mining Techniques: A Survey." In INTERNATIONAL CONFERENCE ON METHODS AND MODELS IN SCIENCE AND TECHNOLOGY (ICM2ST-10). AIP, 2010. http://dx.doi.org/10.1063/1.3526223.

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Bahri, Maroua, Albert Bifet, Silviu Maniu, and Heitor Murilo Gomes. "Survey on Feature Transformation Techniques for Data Streams." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/668.

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Mining high-dimensional data streams poses a fundamental challenge to machine learning as the presence of high numbers of attributes can remarkably degrade any mining task's performance. In the past several years, dimension reduction (DR) approaches have been successfully applied for different purposes (e.g., visualization). Due to their high-computational costs and numerous passes over large data, these approaches pose a hindrance when processing infinite data streams that are potentially high-dimensional. The latter increases the resource-usage of algorithms that could suffer from the curse of dimensionality. To cope with these issues, some techniques for incremental DR have been proposed. In this paper, we provide a survey on reduction approaches designed to handle data streams and highlight the key benefits of using these approaches for stream mining algorithms.
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Ying Wah, The, Mustaffa Kamal Nor, Zaitun Abu Bakar, and Lee Sai Peck. "Data Mining in Computer Auditing." In 2002 Informing Science + IT Education Conference. Informing Science Institute, 2002. http://dx.doi.org/10.28945/2586.

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In this paper, we first introduce the readers about the main function of a computer auditor. This is followed by a description of auditing the usage of stationeries in the Faculty of Computer Science and Information Technology, University of Malaya. It is a very time consuming process to audit all stationeries. Therefore, we introduce the data mining techniques to help us find the relevant stationeries. We use this information to recommend purchasers to purchase relevant items together in order to achieve efficiently in purchasing stationeries process.
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N. Agu, Monica, Stephen Nabareseh, and Christian Nedu Osakwe. "Investigating Web Based Marketing in the Context of Micro and Small-Scale Enterprises (MSEs): A Decision Tree Classification Technique." In InSITE 2015: Informing Science + IT Education Conferences: USA. Informing Science Institute, 2015. http://dx.doi.org/10.28945/2201.

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This paper presents the findings of an exploratory study of web based marketing (WBM) usage predictor variables in the context of micro and small-scale enterprises (MSEs). By means of a cross-sectional field study, a structured questionnaire was used to elicit responses from 267 enterprises situated in the South East Region of Nigeria. The main rationale for this study is to provide a vivid description of pertinent variables that are most likely to influence an enterprise’s consideration of the relevance and/or implementation of WBM. Against this backdrop, the authors used the decision tree classification technique of data mining to build a predictive model. One of the interesting findings in this study seems to show that service-oriented enterprises that have a social media presence and are equally headed by highly educated women have a higher proclivity of engaging in WBM. By and large, our findings provide an understanding of idiosyncratic factors that impact on WBM non (usage) by enterprises. Lastly, our findings have implications for practitioners and policy makers in developing countries, particularly that of Nigeria.
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Madiraju, Praveen, and Yanqing Zhang. "Web usage data mining agent." In AeroSense 2002, edited by Belur V. Dasarathy. SPIE, 2002. http://dx.doi.org/10.1117/12.460231.

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Albanese, Massimiliano, Antonio Picariello, Carlo Sansone, and Lucio Sansone. "A web personalization system based on web usage mining techniques." In the 13th international World Wide Web conference. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1013367.1013439.

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