A Preliminary Analysis of Web Usage Behaviors from Web Access Log Files

2013 ◽  
pp. 325-332
Author(s):  
Thakerng Wongsirichot ◽  
Sukgamon Sukpisit ◽  
Warakorn Hanghu
Keyword(s):  
Preliminary Analysis ◽  
Web Usage ◽  
Log Files ◽  
Web Access

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.

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.

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.

A SCALABLE CLUSTERING METHOD FOR CATEGORICAL SEQUENCE DATA

2005 ◽  
Vol 02 (02) ◽  
pp. 167-180
Author(s):  
SEUNG-JOON OH ◽  
JAE-YEARN KIM
Keyword(s):  
Nearest Neighbor ◽  
Sequence Data ◽  
Clustering Algorithms ◽  
K Nearest Neighbor ◽  
Clustering Method ◽  
Scalable Clustering ◽  
Log Files ◽  
Web Access ◽  
Better Than

Clustering of sequences is relatively less explored but it is becoming increasingly important in data mining applications such as web usage mining and bioinformatics. The web user segmentation problem uses web access log files to partition a set of users into clusters such that users within one cluster are more similar to one another than to the users in other clusters. Similarly, grouping protein sequences that share a similar structure can help to identify sequences with similar functions. However, few clustering algorithms consider sequentiality. In this paper, we study how to cluster sequence datasets. Due to the high computational complexity of hierarchical clustering algorithms for clustering large datasets, a new clustering method is required. Therefore, we propose a new scalable clustering method using sampling and a k-nearest-neighbor method. Using a splice dataset and a synthetic dataset, we show that the quality of clusters generated by our proposed approach is better than that of clusters produced by traditional algorithms.

Web Usage Mining

Web Mining ◽  
2011 ◽  
pp. 373-392 ◽  
Author(s):  
Yew-Kwong Woon ◽  
Wee-Keong Ng ◽  
Ee-Peng Lim
Keyword(s):  
Data Mining ◽  
World Wide ◽  
Web Usage Mining ◽  
Web Usage ◽  
Online Business ◽  
Web Access ◽  
Business Competitiveness ◽  
Access Logs ◽  
Web Server Logs ◽  
Web Access Logs

The rising popularity of electronic commerce makes data mining an indispensable technology for several applications, especially online business competitiveness. The World Wide Web provides abundant raw data in the form of Web access logs. However, without data mining techniques, it is difficult to make any sense out of such massive data. In this chapter, we focus on the mining of Web access logs, commonly known as Web usage mining. We analyze algorithms for preprocessing and extracting knowledge from such logs. We will also propose our own techniques to mine the logs in a more holistic manner. Experiments conducted on real Web server logs verify the practicality as well as the efficiency of the proposed techniques as compared to an existing technique. Finally, challenges in Web usage mining are discussed.

Web Usage Minning using Patterns with Different Algorithm

2017 ◽  
pp. 1-9
Author(s):  
Keyword(s):  
Data Mining ◽  
Web Usage Mining ◽  
Data Discovery ◽  
Web Log ◽  
Data Usage ◽  
Web Usage ◽  
Log Files ◽  
Content Mining ◽  
User Data ◽  
Data Content

Web usage mining is a part of data mining. Data usage mining is divided into three parts 1) Data content mining 2) Data structured mining 3) Data usage mining. In this paper I am discussing about log files which are used in data usage mining. Log files are used to store user’s activity in web server using websites. So that websites can be improved by gathering user data. Web usage mining having three sub parts which is reprocessing, data discovery and data analysis. Further, in this paper, details about web log files are discussed. Three algorithms are discussed which are used for patterns of log files. There comparison is showed in this paper with the help of graphs.

Constructive and Dysfunctional Personal Web Usage in the Workplace

2004 ◽  
pp. 1-27 ◽  
Author(s):  
Murugan Anandarajan ◽  
Claire A. Simmers
Keyword(s):  
Q Methodology ◽  
Concept Maps ◽  
Working Hours ◽  
Important Variable ◽  
Dual Nature ◽  
Web Usage ◽  
Organizational Position ◽  
Web Access ◽  
The Web ◽  
Personal Web Usage

In order to better understand how people work in the Web-enabled workplace, we examined the phenomenon of personal Web usage (PWU). We analyzed 316 responses from those with Web access at work to the question, “Do you think it’s ok for a person to use the Web for non-work purposes during working hours in the workplace.” The responses were coded into 19 themes and four categories. Using correspondence analysis, concept maps were generated which revealed that personal Web usage in the workplace is a complex issue with not only a potentially dysfunctional dimension, but also a potentially constructive one. Organizational position was an important variable with top, middle, lower-level managers, as well as professionals, and administrators positioning in different spaces on the conceptual map. Further analysis using Q-methodology reinforced the dual nature of PWU and the importance of position. Drawing on our results, an extension of the social contract theory and a model of personal Web usage in the workplace were suggested.

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