A Lattice-Based Framework for Interactively and Incrementally Mining Web Traversal Patterns

2008 ◽  
pp. 72-96
Author(s):  
Yue-Shi Lee ◽  
Show-Jane Yen
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Web Data ◽  
Minimum Support ◽  
User Behaviors ◽  
Web Logs ◽  
Interactive Data Mining ◽  
Support Threshold ◽  
Interactive Data ◽  
The Web

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 difficult to select a perfect minimum support threshold during the mining process to find the interesting rules. Even though the experienced experts, they also cannot determine the appropriate minimum support. Thus, we must constantly adjust the minimum support until the satisfactory mining results can be found. The essences of incremental or interactive data mining are that we can use the previous mining results to reduce the unnecessary processes when the minimum support is changed or web logs are updated. In this paper, we propose efficient incremental and interactive data mining algorithms to discover web traversal patterns and make the mining results to satisfy the users’ requirements. The experimental results show that our algorithms are more efficient than the other approaches.

A Lattice-Based Framework for Interactively and Incrementally Mining Web Traversal Patterns

2010 ◽  
pp. 448-467
Author(s):  
Yue-Shi Lee
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Web Data ◽  
Minimum Support ◽  
User Behaviors ◽  
Web Logs ◽  
Interactive Data Mining ◽  
Support Threshold ◽  
Interactive Data ◽  
The Web

Web mining is one of the mining technologies, which applies data mining techniques in large amounts 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 difficult to select a perfect minimum support threshold during the mining process to find the interesting rules. Even the experienced experts also cannot determine the appropriate minimum support. Thus, we must constantly adjust the minimum support until the satisfactory mining results can be found. The essences of incremental or interactive data mining are that we can use the previous mining results to reduce the unnecessary processes when the minimum support is changed or Web logs are updated. In this chapter, we propose efficient incremental and interactive data mining algorithms to discover Web traversal patterns and make the mining results to satisfy the users’ requirements. The experimental results show that our algorithms are more efficient than the other approaches.

Ubiquitous Mining with Interactive Data Mining Agents

2009 ◽  
Vol 24 (6) ◽  
pp. 1018-1027
Author(s):  
Xin-Dong Wu ◽  
Xing-Quan Zhu ◽  
Qi-Jun Chen ◽  
Fei-Yue Wang
Keyword(s):  
Data Mining ◽  
Interactive Data Mining ◽  
Interactive Data

scholarly journals Real-time interactive data mining for chemical imaging information: application to automated histopathology

2013 ◽  
Vol 14 (1) ◽  
pp. 156 ◽  
Author(s):  
David Mayerich ◽  
Michael Walsh ◽  
Matthew Schulmerich ◽  
Rohit Bhargava
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Chemical Imaging ◽  
Interactive Data Mining ◽  
Interactive Data

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.

Effectiveness of Web Usage Mining Techniques in Business Application

2017 ◽  
pp. 324-350 ◽  
Author(s):  
Ahmed El Azab ◽  
Mahmood A. Mahmood ◽  
Abd El-Aziz
Keyword(s):  
Data Mining ◽  
Academic Research ◽  
Web Usage Mining ◽  
Web Pages ◽  
Web Data ◽  
Web Data Mining ◽  
Web Usage ◽  
Business Application ◽  
Common Interests ◽  
The Web

Web usage mining techniques and applications across industries is still exploratory and, despite an increase in academic research, there are challenge of analyze web which quantitatively capture web users' common interests and characterize their underlying tasks. This chapter addresses the problem of how to support web usage mining techniques and applications across industries by combining language of web pages and algorithms that used in web data mining. Existing research in web usage mining techniques tend to focus on finding out how each techniques can apply in different industries fields. However, there is little evidence that researchers have approached the issue of web usage mining across industries. Consequently, the aim of this chapter is to provide an overview of how the web usage mining techniques and applications across industries can be supported.

scholarly journals Personalized web search on e-commerce using ontology based association mining

2017 ◽  
Vol 7 (1.1) ◽  
pp. 286
Author(s):  
B. Sekhar Babu ◽  
P. Lakshmi Prasanna ◽  
P. Vidyullatha
Keyword(s):  
Data Mining ◽  
Web Search ◽  
Large Data ◽  
Association Mining ◽  
Data Sets ◽  
Data Mining Algorithm ◽  
Web Data ◽  
Data Mining Technique ◽  
Web Data Mining ◽  
The Web

 In current days, World Wide Web has grown into a familiar medium to investigate the new information, Business trends, trading strategies so on. Several organizations and companies are also contracting the web in order to present their products or services across the world. E-commerce is a kind of business or saleable transaction that comprises the transfer of statistics across the web or internet. In this situation huge amount of data is obtained and dumped into the web services. This data overhead tends to arise difficulties in determining the accurate and valuable information, hence the web data mining is used as a tool to determine and mine the knowledge from the web. Web data mining technology can be applied by the E-commerce organizations to offer personalized E-commerce solutions and better meet the desires of customers. By using data mining algorithm such as ontology based association rule mining using apriori algorithms extracts the various useful information from the large data sets .We are implementing the above data mining technique in JAVA and data sets are dynamically generated while transaction is processing and extracting various patterns.

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