MINING WEB DATA USING CLUSTERING TECHNIQUE FOR WEB PERSONALIZATION

2002 ◽  
Vol 02 (03) ◽  
pp. 255-265 ◽  
Author(s):  
SUPRIYA KUMAR DE ◽  
P. RADHA KRISHNA
Keyword(s):  
Dimensional Space ◽  
Web Personalization ◽  
Web Data ◽  
Concept Hierarchy ◽  
Web Usage ◽  
Hierarchy Model ◽  
Dimension Space ◽  
The Relationship ◽  
The Web ◽  
Usage Data

Clustering of data in a large dimension space is of great interest in many data mining applications. In this paper, we propose a method for clustering of web usage data in a high-dimensional space based on a concept hierarchy model. In this method, the relationship present in the web usage data are mapped into a fuzzy proximity relation of user transactions. We also described an approach to present the preference set of URLs to a new user transaction based on the match score with the clusters. The study demonstrates that our approach is general and effective for mining the web data for web personalization.

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.

On the Use of Soft Computing Techniques for Web Personalization

2011 ◽  
pp. 2381-2402
Author(s):  
G. Castellano ◽  
A.M. Fanelli ◽  
M.A. Torsello
Keyword(s):  
Soft Computing ◽  
Domain Knowledge ◽  
User Preferences ◽  
Web Personalization ◽  
Web Based ◽  
Hybrid Schemes ◽  
Web Usage ◽  
Neuro Fuzzy ◽  
Soft Computing Techniques ◽  
Usage Data

Due to the growing variety and quantity of information available on the Web, there is urgent need for developing Web-based applications capable of adapting their services to the needs of the users. This is the main rationale behind the flourishing area of Web personalization that finds in soft computing (SC) techniques a valid tool to handle uncertainty in Web usage data and develop Web-based applications tailored to user preferences. The main reason for this success seems to be the synergy resulting from SC paradigms, such as fuzzy logic, neural networks, and genetic algorithms. Each of these computing paradigms provides complementary reasoning and searching methods that allow the use of domain knowledge and empirical data to solve complex problems. In this chapter, we emphasize the suitability of hybrid schemes combining different SC techniques for the development of effective Web personalization systems. In particular, we present a neuro-fuzzy approach for Web personalization that combines techniques from the fuzzy and the neural paradigms to derive knowledge from Web usage data and represent the knowledge in the comprehensible form of fuzzy rules. The derived knowledge is ultimately used to dynamically suggest interesting links to the user of a Web site.

Web Usage Mining Issues in Big Data

2021 ◽  
pp. 102-112
Author(s):  
Sunny Sharma ◽  
Manisha Malhotra
Keyword(s):  
Data Mining ◽  
Big Data ◽  
User Behavior ◽  
Web Usage Mining ◽  
Web Personalization ◽  
Data Mining Techniques ◽  
Meaningful Information ◽  
Web Usage ◽  
Use Of Data ◽  
The Web

Web usage mining is the use of data mining techniques to analyze user behavior in order to better serve the needs of the user. This process of personalization uses a set of techniques and methods for discovering the linking structure of information on the web. The goal of web personalization is to improve the user experience by mining the meaningful information and presented the retrieved information in a way the user intends. The arrival of big data instigated novel issues to the personalization community. This chapter provides an overview of personalization, big data, and identifies challenges related to web personalization with respect to big data. It also presents some approaches and models to fill the gap between big data and web personalization. Further, this research brings additional opportunities to web personalization from the perspective of big data.

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.

UNDERSTANDING DIFFERENCES IN WEB USAGE: THE ROLE OF NEED FOR COGNITION AND THE FIVE FACTOR MODEL OF PERSONALITY

2001 ◽  
Vol 29 (4) ◽  
pp. 391-398 ◽  
Author(s):  
Tracy L. Tuten ◽  
Michael Bosnjak
Keyword(s):  
Product Information ◽  
Factor Model ◽  
Five Factor Model ◽  
Need For Cognition ◽  
Openness To Experience ◽  
Web Usage ◽  
Five Factors ◽  
The Relationship ◽  
The Web

Using the Five-factor model of personality and Need for Cognition, the authors investigated the relationship between personality and Web usage. Of the five factors, Openness to Experience and Neuroticism showed the greatest association to Web usage. Openness to Experience was positively related to using the Web for entertainment and product information, while Neuroticism was negatively related to Web usage. Need for Cognition was significantly and positively correlated with all Web activities involving cognitive thought.

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 FARMING WITH CLICKSTREAM

2008 ◽  
Vol 07 (02) ◽  
pp. 291-308 ◽  
Author(s):  
JIA HU ◽  
NING ZHONG
Keyword(s):  
Behavior Analysis ◽  
Web Mining ◽  
User Interaction ◽  
Purchasing Behavior ◽  
Web Content ◽  
Related Data ◽  
Web Usage ◽  
Proposed Model ◽  
The Web ◽  
Usage Data

In a commercial website or portal, Web information fusion is usually from the following two approaches, one is to integrate the Web content, structure, and usage data for surfing behavior analysis; the other is to integrate Web usage data with traditional customer, product, and transaction data for purchasing behavior analysis. In this paper, we propose a unified model based on Web farming technology for collecting clickstream logs in the whole user interaction process. We emphasize that collecting clickstream logs at the application layer will help to seamlessly integrate Web usage data with other customer-related data sources. In this paper, we extend the Web log standard to modeling clickstream format and Web mining to Web farming from passively collecting data and analyzing the customer behavior to actively influence the customer's decision making. The proposed model can be developed as a common plugin for most existing commercial websites and portals.

A Discrete Artificial Bees Colony Inspired Biclustering Algorithm

2012 ◽  
Vol 3 (1) ◽  
pp. 30-42 ◽  
Author(s):  
R. Rathipriya ◽  
K. Thangavel
Keyword(s):  
Fitness Function ◽  
Merit Function ◽  
Discrete Version ◽  
Binary Particle Swarm Optimization ◽  
Data Mining Technique ◽  
Artificial Bee Colony Optimization ◽  
Web Usage ◽  
Bee Colony ◽  
The Web ◽  
Usage Data

Biclustering methods are the potential data mining technique that has been suggested to identify local patterns in the data. Biclustering algorithms are used for mining the web usage data which can determine a group of users which are correlated under a subset of pages of a web site. Recently, many blistering methods based on meta-heuristics have been proposed. Most use the Mean Squared Residue as merit function but interesting and relevant patterns such as shifting and scaling patterns may not be detected using this measure. However, it is important to discover this type of pattern since commonly the web users can present a similar behavior although their interest levels vary in different ranges or magnitudes. In this paper a new correlation based fitness function is designed to extract shifting and scaling browsing patterns. The proposed work uses a discrete version of Artificial Bee Colony optimization algorithm for biclustering of web usage data to produce optimal biclusters (i.e., highly correlated biclusters). It’s demonstrated on real dataset and its results show that proposed approach can find significant biclusters of high quality and has better convergence performance than Binary Particle Swarm Optimization (BPSO).

Web Personalization Using Clustering of Web Usage Data

2014 ◽  
Vol 4 (5) ◽  
pp. 69-84
Author(s):  
Bhawesh Kumar Thakur ◽  
Syed Qamar Abbas ◽  
Mohd.Rizwan Beg
Keyword(s):  
Web Personalization ◽  
Web Usage ◽  
Usage Data
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