Relevant bibliographies by topics / Web Usage Mining; Data Mining Algorithms; Mining Techniques and Pattern Discovery

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  1. Journal articles
  2. Book chapters
  3. Conference papers

Academic literature on the topic 'Web Usage Mining; Data Mining Algorithms; Mining Techniques and Pattern Discovery'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Web Usage Mining; Data Mining Algorithms; Mining Techniques and Pattern Discovery"

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Et. al., V. Aruna,. "A Review on Design and Development Of Sequential Patterns Algorithms In Web Usage Mining." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 2 (2021): 1634–39. http://dx.doi.org/10.17762/turcomat.v12i2.1448.

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In the recent years with the advancement in technology, a lot of information is available in different formats and extracting the knowledge from that data has become a very difficult task. Due to the vast amount of information available on the web, users are finding it difficult to extract relevant information or create new knowledge using information available on the web. To solve this problem Web mining techniques are used to discover the interesting patterns from the hidden data .Web Usage Mining (WUM), which is one of the subset of Web Mining helps in extracting the hidden knowledge presen
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Ng, Qi Yau, and Mohd Nazmee Wan Zainon Wan. "UNDERSTANDING WEB TRAFFIC ACTIVITIES USING WEB MINING TECHNIQUES." International Journal of Engineering Technologies and Management Research 4, no. 9 (2017): 18–26. https://doi.org/10.5281/zenodo.1006814.

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<strong><em>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-mini
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Gul, Sumeer, Shohar Bano, and Taseen Shah. "Exploring data mining: facets and emerging trends." Digital Library Perspectives 37, no. 4 (2021): 429–48. http://dx.doi.org/10.1108/dlp-08-2020-0078.

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Purpose Data mining along with its varied technologies like numerical mining, textual mining, multimedia mining, web mining, sentiment analysis and big data mining proves itself as an emerging field and manifests itself in the form of different techniques such as information mining; big data mining; big data mining and Internet of Things (IoT); and educational data mining. This paper aims to discuss how these technologies and techniques are used to derive information and, eventually, knowledge from data. Design/methodology/approach An extensive review of literature on data mining and its allie
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N.Sanfia, Sehnaz* Dr.I.Elizabeth Shanthi. "MINING OF WEB LOG FILES USING RELEVANT COMPUTING TECHNIQUES FOR IMPROVING FUTURE ANTICIPATION USAGE OF WEB NAVIGATION." INTERNATIONAL JOURNAL OF ENGINEERING SCIENCES & RESEARCH TECHNOLOGY 5, no. 5 (2016): 536–41. https://doi.org/10.5281/zenodo.51534.

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The Internet has evolved extensively over the past few decades. Web navigation refers to the process of navigating a network of information resources in the World Wide Web, which is organized as hypertext or hypermedia.&nbsp; The navigation related to web navigation usability gets solved by comparing the actual and anticipated usage patterns. The actual usage pattern removed from web server logs are sporadically recorded in operational websites for handling the log data. This process is used to identify the users, user session and user task oriented transactions. The pattern can be discovered
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Ali Mohammed, Mohammed, Hala Abdulsalam jasim, and Ahmed Oday. "Discussion on techniques of data cleaning, user identification, and session identification phases of web usage mining from 2000 to 2022." Iraqi Journal for Computers and Informatics 51, no. 1 (2025): 37–51. https://doi.org/10.25195/ijci.v51i1.549.

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The data preprocessing step is an important step in web usage mining because of the nature of log data, which are heterogeneous, unstructured, and noisy. Given the scalability and efficiency of algorithms in pattern discovery, a preprocessing step must be applied. In this study, the sequential methodologies utilized in the preprocessing of data from web server logs, with an emphasis on sub-phases, such as session identification, user identification, and data cleansing, are comprehensively evaluated and meticulously examined.
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Pradip, Suresh Mane, Kumar Jetawat Ashok, and Jagannath Nikumbh Pravin. "Web Page Recommendation using Random Forest with Fire Fly Algorithm in Web Mining." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 3 (2020): 499–505. https://doi.org/10.35940/ijeat.B4442.029320.

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Nowadays, internet has become the easiest way to obtain more information from the web and millions of users search internet to find out the information. The continuous growth of web pages and users interest to search more information about various topics increases the complexity of recommendation. The user&#39;s behavior is extracted by using the web mining techniques, which are used in web server log. The main aim of this research study is to identify the navigation pattern of users from the log files. There are three major steps in the web mining process namely pre-processing the data, class
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Merin, J. Brindha, Dr W. Aisha Banu, Akila R., and Radhika A. "Semantic Annotation Based Mechanism for Web Service Discovery and Recommendation." Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications 14, no. 3 (2023): 169–85. http://dx.doi.org/10.58346/jowua.2023.i3.013.

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Web Mining is regarded as one among the data mining techniques that aids in fetching and extraction of necessary data from the web. Conversely, Web usage mining discovers and extracts essential patterns usage over the webs which are being further utilized by various web applications. In order to discover and explore web services that are registered with documents of Web Services-Inspection, Discovery and Integration registry, Universal Description wants specific search circumstance similar to URL, category and service name. The document of Web Service Description Language (WSDL) offers a condi
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Selvam, S. "Invention a Paradigm to Discovery the Network Navigation Using Poisson Distribution." Asian Journal of Electrical Sciences 9, no. 2 (2021): 9–12. http://dx.doi.org/10.51983/ajes-2020.9.2.2550.

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Due to increasing the act of Applied science College in Tamil Nadu, the level of competition for admission price is also increased. By implementing some dynamic strategies only the academic introduction s can meet their own competition. One survey clearly commonwealth that more than 75% of the Engineering Colleges their forcefulness is less than thirty % of their actual intake. Hence the surveillance is the job for the insane asylum s. One more survey shows that every year 10% of the applied science college’ windup their affiliation and blessing due to lack of admittance, and 5% of the enginee
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J., Umarani, and S. Manikandan Dr. "PATTERN DISCOVERY TECHNIQUES IN WEB USAGE MINING." International Journal of Scientific Research and Modern Education 3, no. 2 (2018): 1–3. https://doi.org/10.5281/zenodo.1332044.

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WWW is a very popular and interactive medium for broadcasting information today. Due to the vast, diverse and lively nature of web it advancesthe scalability, multimedia data and temporal issues respectively. The development of the web has given rise to large quantity of data that is freely available for user access.Web Usage Mining enhances the user experience while browsing web pages by using past history of web data. It also used to improve the web site navigation. Web mining makes use of data mining techniques and deciphers potentially useful information from web data. Web usage mining is
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Abraham, Ajith. "Business Intelligence from Web Usage Mining." Journal of Information & Knowledge Management 02, no. 04 (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, personal
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Book chapters on the topic "Web Usage Mining; Data Mining Algorithms; Mining Techniques and Pattern Discovery"

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Kumar, Manish, and Sumit Kumar. "Rule Optimization of Web-Logs Data Using Evolutionary Technique." In Data Mining and Analysis in the Engineering Field. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-6086-1.ch010.

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Web usage mining can extract useful information from Weblogs to discover user access patterns of Web pages. Web usage mining itself can be classified further depending on the kind of usage data. This may consider Web server data, application server data, or application level data. Web server data corresponds to the user logs that are collected at Web servers. Some of the typical data collected at Web server are the URL requested, the IP address from which the request originated, and timestamp. Weblog data is required to be cleaned, condensed, and transformed in order to retrieve and analyze si
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Banu, P. K. Nizar, and H. Inbarani. "Analysis of Click Stream Patterns using Soft Biclustering Approaches." In Systems Approach Applications for Developments in Information Technology. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-1562-5.ch015.

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As websites increase in complexity, locating needed information becomes a difficult task. Such difficulty is often related to the websites’ design but also ineffective and inefficient navigation processes. Research in web mining addresses this problem by applying techniques from data mining and machine learning to web data and documents. In this study, the authors examine web usage mining, applying data mining techniques to web server logs. Web usage mining has gained much attention as a potential approach to fulfill the requirement of web personalization. In this paper, the authors propose K-
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Yao, Jenq-Foung, and Yongqiao Xiao. "Traversal Pattern Mining in Web Usage Data." In Data Warehousing and Mining. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-951-9.ch119.

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Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are als
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Rao, T. Venkat Narayana, and D. Hiranmayi. "Methodologies and Techniques of Web Usage Mining." In Advances in Data Mining and Database Management. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0613-3.ch011.

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Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. It is the type of Web mining activity that involves the automatic discovery of out what users are looking for on the Internet. In this chapter methodology of web usage mining explained in detail which are data collection, data preprocessing, knowledge discovery and pattern analysis. The different Web Usage Mining techniques are described, which are used for knowledge and pattern discovery. These are statistical analysis, sequential patterns, classification, a
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Xiao, Yongqiao, and Jenq-Foung (J F. ). Yao. "Traversal Pattern Mining in Web Usage Data." In Web Information Systems. IGI Global, 2004. http://dx.doi.org/10.4018/978-1-59140-208-4.ch010.

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Web usage mining is to discover useful patterns in the web usage data, and the patterns provide useful information about the user’s browsing behavior. This chapter examines different types of web usage traversal patterns and the related techniques used to uncover them, including Association Rules, Sequential Patterns, Frequent Episodes, Maximal Frequent Forward Sequences, and Maximal Frequent Sequences. As a necessary step for pattern discovery, the preprocessing of the web logs is described. Some important issues, such as privacy, sessionization, are raised, and the possible solutions are als
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Abdalla, Abubakr Gafar, Tarig Mohamed Ahmed, and Mohamed Elhassan Seliaman. "Web Usage Mining and the Challenge of Big Data." In Big Data. IGI Global, 2016. http://dx.doi.org/10.4018/978-1-4666-9840-6.ch042.

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The web is a rich data mining source which is dynamic and fast growing, providing great opportunities which are often not exploited. Web data represent a real challenge to traditional data mining techniques due to its huge amount and the unstructured nature. Web logs contain information about the interactions between visitors and the website. Analyzing these logs provides insights into visitors' behavior, usage patterns, and trends. Web usage mining, also known as web log mining, is the process of applying data mining techniques to discover useful information hidden in web server's logs. Web l
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Hu, Wen-Chen, Yanjun Zuo, Lei Chen, and Chyuan-Huei Thomas Yang. "Adaptive Mobile Web Browsing Using Web Mining Technologies." In Business Web Strategy. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-60566-024-0.ch010.

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Using mobile handheld devices such as smart cellular phones and personal digital assistants (PDAs) to browse the mobile Internet is a trend of Web browsing. However, the small screens of handheld devices and slow mobile data transmission make the mobile Web browsing awkward. This research applies Web usage mining technologies to adaptive Web viewing for handheld devices. Web usage mining is the application of data mining techniques to the usage logs of large Web data repositories in order to produce results that can be applied to many practical subjects, such as improving Web sites/pages. A We
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Abdalla, Abubakr Gafar, Tarig Mohamed Ahmed, and Mohamed Elhassan Seliaman. "Web Usage Mining and the Challenge of Big Data." In Handbook of Research on Trends and Future Directions in Big Data and Web Intelligence. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-8505-5.ch020.

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The web is a rich data mining source which is dynamic and fast growing, providing great opportunities which are often not exploited. Web data represent a real challenge to traditional data mining techniques due to its huge amount and the unstructured nature. Web logs contain information about the interactions between visitors and the website. Analyzing these logs provides insights into visitors' behavior, usage patterns, and trends. Web usage mining, also known as web log mining, is the process of applying data mining techniques to discover useful information hidden in web server's logs. Web l
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Ding, Qin, and Gnanasekaran Sundarraj. "Mining Association Rules from XML Data." In Data Mining and Knowledge Discovery Technologies. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-960-1.ch003.

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With the growing usage of XML in the World Wide Web and elsewhere as a standard for the exchange of data and to represent semi-structured data, there is an imminent need for tools and techniques to perform data mining on XML documents and XML repositories. In this chapter, we propose a framework for association rule mining on XML data. We present a Java-based implementation of the Apriori and the FP-Growth algorithms for this task and compare their performances. We also compare the performance of our implementation with an XQuery-based implementation.
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Lee, Yue-Shi, and Show-Jane Yen. "A Lattice-Based Framework for Interactively and Incrementally Mining Web Traversal Patterns." In Data Mining and Knowledge Discovery Technologies. IGI Global, 2008. http://dx.doi.org/10.4018/978-1-59904-960-1.ch004.

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Web mining is one of the mining technologies, which applies data mining techniques in large amount of web data to improve the web services. Web traversal pattern mining discovers most of the users’ access patterns from web logs. This information can provide the navigation suggestions for web users such that appropriate actions can be adopted. However, the web data will grow rapidly in the short time, and some of the web data may be antiquated. The user behaviors may be changed when the new web data is inserted into and the old web data is deleted from web logs. Besides, it is considerably diff
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Conference papers on the topic "Web Usage Mining; Data Mining Algorithms; Mining Techniques and Pattern Discovery"

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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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