scholarly journals ANALISIS WEB USAGE MINING MENGGUNAKAN METODE MODIFIED GUSTAFSON – KESSEL CLUSTERING DAN ASSOCIATION RULE PADA WEBSITE UNIVERSITAS DIPONEGORO

2020 ◽  
Vol 9 (4) ◽  
pp. 486-494
Author(s):  
Galuh Nurvinda Kurniawati ◽  
Rukun Santoso ◽  
Sugito Sugito
Keyword(s):  
Association Rule ◽  
Web Usage Mining ◽  
Engineering Faculty ◽  
Web Log ◽  
Web Usage ◽  
Log File ◽  
The Web

The comprehension of web visitors patterns are needed to develop website in an optimal fashion. The visitor pattern contained in the web log file of Diponegoro University’s website is clustered by Modified Gustafson-Kessel method. In general, this method produces two until six clusters. Two kinds of results are outlined in this paper. The first is the result contains two clusters, and the second is containing three clusters. In the first result, the visitors are divided into information seekers of student capacity and Engineering Faculty. In the second result, the visitors are divided into information seekers of Medicine Faculty, student admission and Engineering Faculty.  

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.

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.

Extracting Knowledge from Web Data

2014 ◽  
Vol 7 (4) ◽  
pp. 27-41
Author(s):  
Hanane Ezzikouri ◽  
Mohamed Fakir ◽  
Cherki Daoui ◽  
Mohamed Erritali
Keyword(s):  
Web Mining ◽  
User Behavior ◽  
Data Extraction ◽  
Research Area ◽  
Web Usage Mining ◽  
Web Data ◽  
Main Research ◽  
Web Log ◽  
Web Usage ◽  
Log File

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.

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 Web Usage Mining Approach to User Navigation Pattern and Prediction in Web Log Data

2012 ◽  
Vol 3 (4) ◽  
pp. 92-94
Author(s):  
SUJATHA PADMAKUMAR ◽  
◽  
Dr.PUNITHAVALLI Dr.PUNITHAVALLI ◽  
Dr.RANJITH Dr.RANJITH
Keyword(s):  
Web Usage Mining ◽  
Log Data ◽  
Web Log ◽  
Web Usage ◽  
User Navigation

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.

scholarly journals Application of Data Mining on Web Usage Data for Security: WebSecuDMiner

2019 ◽  
Author(s):  
Muhammad Zia Aftab Khan ◽  
Jihyun Park
Keyword(s):  
Design Methodology ◽  
Access Pattern ◽  
User Research ◽  
Web Log ◽  
Web Access ◽  
User Access ◽  
Log File ◽  
Access Patterns ◽  
Web Access Pattern ◽  
The Web

The purpose of this paper is to develop WebSecuDMiner algorithm to discover unusual web access patterns based on analysing the potential rules hidden in web server log and user navigation history. Design/methodology/approach: WebSecuDMiner uses equivalence class transformation (ECLAT) algorithm to extract user access patterns from the web log data, which will be used to identify the user access behaviours pattern and detect unusual one. Data extracted from the web serve log and user browsing behaviour is exploited to retrieve the web access pattern that is produced by the same user. Findings: WebSecuDMiner is used to detect whether any unauthorized access have been posed and take appropriate decisions regarding the review of the original rights of suspicious user. Research limitations/implications: The present work uses the database which is extracted from web serve log file and user browsing behaviour. Although the page is viewed by the user, the visit is not recorded in the server log file, since it can be access from the browser's cache.

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