scholarly journals Web Usage Mining: A Survey on Pattern Extraction from Web Logs

2011 ◽  
pp. 15-23
Author(s):  
S. K. Pani ◽  
L. Panigrahy ◽  
V.H. Sankar ◽  
A.K. Manda ◽  
S.K. Padhi ◽  
...  
Keyword(s):  
Web Usage Mining ◽  
Pattern Extraction ◽  
User Behaviour ◽  
Web Usage ◽  
Log Files ◽  
Web Logs ◽  
Interesting Pattern ◽  
Web Access ◽  
The Web

As the size of web increases along with number of users, it is very much essential for the website owners to better understand their customers so that they can provide better service, and also enhance the quality of the website. To achieve this they depend on the web access log files. The web access log files can be mined to extract interesting pattern so that the user behaviour can be understood. This paper presents an overview of web usage mining and also provides a survey of the pattern extraction algorithms used for web usage mining.

Web Usage Mining and the Challenge of Big Data

Big Data ◽  
2016 ◽  
pp. 899-928
Author(s):  
Abubakr Gafar Abdalla ◽  
Tarig Mohamed Ahmed ◽  
Mohamed Elhassan Seliaman
Keyword(s):  
Data Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Data Mining Techniques ◽  
Web Log ◽  
Web Usage ◽  
Web Logs ◽  
Usage Patterns ◽  
Rich Data ◽  
The Web

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 logs are primarily used by Web administrators to know how much traffic they get and to detect broken links and other types of errors. Web usage mining extracts useful information that can be beneficial to a number of application areas such as: web personalization, website restructuring, system performance improvement, and business intelligence. The Web usage mining process involves three main phases: pre-processing, pattern discovery, and pattern analysis. Various preprocessing techniques have been proposed to extract information from log files and group primitive data items into meaningful, lighter level abstractions that are suitable for mining, usually in forms of visitors' sessions. Major data mining techniques in web usage mining pattern discovery are: clustering, association analysis, classification, and sequential patterns discovery. This chapter discusses the process of web usage mining, its procedure, methods, and patterns discovery techniques. The chapter also presents a practical example using real web log data.

Traversal Pattern Mining in Web Usage Data

2008 ◽  
pp. 2004-2021
Author(s):  
Jenq-Foung Yao ◽  
Yongqiao Xiao
Keyword(s):  
Pattern Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Web Usage ◽  
Web Logs ◽  
Frequent Episodes ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

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 also discussed.

They Know What You Will Do Next Click

2020 ◽  
pp. 100-122
Author(s):  
Serra Çelik
Keyword(s):  
Focus Group ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Log ◽  
Web Usage ◽  
User Behaviors ◽  
Log Files ◽  
The Web

This chapter focuses on predicting web user behaviors. When web users enter a website, every move they make on that website is stored as web log files. Unlike the focus group or questionnaire, the log files reflect real user behavior. It can easily be said that having actual user behavior is a gold value for the organizations. In this chapter, the ways of extracting user patterns (user behavior) from the log files are sought. In this context, the web usage mining process is explained. Some web usage mining techniques are mentioned.

Traversal Pattern Mining in Web Usage Data

2004 ◽  
pp. 335-358 ◽  
Author(s):  
Yongqiao Xiao ◽  
Jenq-Foung (J.F.) Yao
Keyword(s):  
Pattern Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Web Usage ◽  
Web Logs ◽  
Frequent Episodes ◽  
Browsing Behavior ◽  
The Web ◽  
Usage Data

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 also discussed.

Web Usage Mining and the Challenge of Big Data

2015 ◽  
pp. 418-447
Author(s):  
Abubakr Gafar Abdalla ◽  
Tarig Mohamed Ahmed ◽  
Mohamed Elhassan Seliaman
Keyword(s):  
Data Mining ◽  
Pattern Discovery ◽  
Web Usage Mining ◽  
Data Mining Techniques ◽  
Web Log ◽  
Web Usage ◽  
Web Logs ◽  
Usage Patterns ◽  
Rich Data ◽  
The Web

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 logs are primarily used by Web administrators to know how much traffic they get and to detect broken links and other types of errors. Web usage mining extracts useful information that can be beneficial to a number of application areas such as: web personalization, website restructuring, system performance improvement, and business intelligence. The Web usage mining process involves three main phases: pre-processing, pattern discovery, and pattern analysis. Various preprocessing techniques have been proposed to extract information from log files and group primitive data items into meaningful, lighter level abstractions that are suitable for mining, usually in forms of visitors' sessions. Major data mining techniques in web usage mining pattern discovery are: clustering, association analysis, classification, and sequential patterns discovery. This chapter discusses the process of web usage mining, its procedure, methods, and patterns discovery techniques. The chapter also presents a practical example using real web log data.

Construction of User’s Navigation Sessions from Web Logs for Web Usage Mining

2020 ◽  
Vol 17 (9) ◽  
pp. 4432-4437
Author(s):  
Ramakrishnan M. Ramanathaiah ◽  
Bhawna Nigam ◽  
M. Niranjanamurthy
Keyword(s):  
Web Sites ◽  
Web Usage Mining ◽  
Pattern Detection ◽  
Web Log ◽  
Web Usage ◽  
Pull Out ◽  
Web Logs ◽  
Record Data ◽  
Significant Process ◽  
The Web

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.

scholarly journals ANALISIS POLA PERMINTAAN PUBLIKASI DATA BADAN PUSAT STATISTIK MENGGUNAKAN ASSOCIATION RULE APRIORI

2020 ◽  
Vol 7 (2) ◽  
pp. 187
Author(s):  
Farid Ridho ◽  
Fachruddin Mansyur
Keyword(s):  
Data Mining ◽  
Association Rule ◽  
Web Usage Mining ◽  
Mining Method ◽  
Data Mining Techniques ◽  
Web Usage ◽  
Usage Patterns ◽  
Mining Association Rule ◽  
The Web

<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>

A Survey of Web-Usage Mining

2011 ◽  
pp. 125-149 ◽  
Author(s):  
Martha Koutri ◽  
Nikolaos Avouris ◽  
Sophia Daskalaki
Keyword(s):  
Pattern Mining ◽  
User Model ◽  
Web Usage Mining ◽  
Web Usage ◽  
Log Files ◽  
Adaptive Hypermedia Systems ◽  
Usage Patterns ◽  
Web Access ◽  
Access Data ◽  
Mining Association Rule

This chapter discusses Web usage mining techniques that can be applied for building adaptive hypermedia systems. These techniques are used for uncovering hidden patterns within Web access data and then for building the user model that lies in the heart of each adaptive system. Web access data, traditionally stored in the server log files, constitute a rich source of data collected in a non-intrusive way that guards the privacy of users. Several Web usage mining approaches have been proposed for exposing usage patterns, with the most prominent ones being cluster mining, association rule mining, and sequential pattern mining. This chapter provides an overview of the state of the art in research of Web usage mining, and discusses the most relevant criteria for deciding on the suitability of these techniques for building an adaptive Web site. Moreover, the different types of patterns revealed from Web usage mining are correlated with different adaptation aspects.

Taxonomical Classification of Web Usage Mining Applications and its Ontological Representation

2018 ◽  
Vol 7 (3) ◽  
pp. 39-43
Author(s):  
Satyaveer Singh ◽  
Mahendra Singh Aswal
Keyword(s):  
Web Usage Mining ◽  
Web Usage ◽  
Navigation Patterns ◽  
Product Recommendations ◽  
Taxonomical Classification ◽  
The Comparative Study ◽  
The Web ◽  
Real World Problems

Web usage mining is used to find out fascinating consumer navigation patterns which can be applied to a lot of real-world problems, such as enriching websites or pages, generating newly topic or product recommendations and consumer behavior studies, etc. In this paper, an attempt has been made to provide a taxonomical classification of web usage mining applications with two levels of hierarchy. Further, the ontology for various categories of the web usage mining applications has been developed and to prove the completeness of proposed taxonomy, a rigorous case study has been performed. The comparative study with other existing classifications of web usage mining applications has also been performed.

A Java Technology Based Distributed Software Architecture for Web Usage Mining

Web Mining ◽  
2011 ◽  
pp. 355-372
Author(s):  
Juan M. Hernansaez
Keyword(s):  
Inductive Learning ◽  
Mining Area ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Distributed Software ◽  
Web Usage ◽  
Java Technology ◽  
Commercial Company ◽  
Meta Learning ◽  
The Web

In this chapter we focus on the three approaches that seem to be the most successful ones in the Web usage mining area: clustering, association rules and sequential patterns. We will discuss some techniques from each one of these approaches, and then we will show the benefits of using METALA (a META-Learning Architecture) as an integrating tool not only for the discussed Web usage mining techniques, but also for inductive learning algorithms. As we will show, this architecture can also be used to generate new theories and models that can be useful to provide new generic applications for several supervised and non-supervised learning paradigms. As a particular example of a Web usage mining application, we will report our work for a medium-sized commercial company, and we will discuss some interesting properties and conclusions that we have obtained from our reporting.

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