Advances in Web Mining and Web Usage Analysis

2006 ◽  
Keyword(s):  
Web Mining ◽  
Web Usage ◽  
Usage Analysis

Statistical Methods for User Profiling in Web Usage Mining

2010 ◽  
pp. 359-368 ◽  
Author(s):  
Marcello Pecoraro
Keyword(s):  
Statistical Methods ◽  
Web Mining ◽  
Web Usage Mining ◽  
User Profiling ◽  
Web Usage ◽  
Data Object ◽  
Segmentation Methods ◽  
Binary Segmentation ◽  
Usage Analysis ◽  
The Web

This chapter aims at providing an overview about the use of statistical methods supporting the Web Usage Mining. Within the first part is described the framework of the Web Usage Mining as a branch of the Web Mining committed to the study of how to use a Website. Then, the data (object of the analysis) are detailed together with the problems linked to the pre-processing. Once clarified, the data origin and their treatment for a correct development of a Web Usage analysis,the focus shifts on the statistical techniques that can be applied to the analysis background, with reference to binary segmentation methods. Those latter allow the discrimination through a response variable that determines the affiliation of the users to a group by considering some characteristics detected on the same users.

Web Usage Mining

2012 ◽  
Vol 3 (3) ◽  
pp. 1-12 ◽  
Author(s):  
Moiz Uddin Ahmed ◽  
Amjad Mahmood
Keyword(s):  
Web Sites ◽  
Web Mining ◽  
Design Development ◽  
Web Usage ◽  
Distant Learning ◽  
Research Areas ◽  
Usage Patterns ◽  
Usage Analysis ◽  
The University ◽  
The Web

The technological revolutions have opened up new ways of information and communication. The Internet is growing as a vital source of information in this modern era of technology. The ever increasing volume of information through WWW is creating complexity in the design, development and deployment of WWW. It has become important for the organizations to analyze the usage of their web sites. The web usage analysis may help the organizations not only to monitor the load on their websites and cater for the needs of their potential clients but also enhance their web services and restructure the organization to better serve their clients. Web mining has emerged as important research areas used to discover information which can be utilized for improvement of websites. Allama Iqbal Open University (AIOU) is one of the largest open and distant university of the world. Due to unique philosophy of open and distant learning, AIOU has been providing useful information online through its website. It is an active website which is flooded with huge flow of information. This paper presents web usage analysis of AIOU website and provides statistical analysis of the usage patterns. It presents how the results were used not only to enhance the web contents and services but also discusses how these results helped the university to allocate and reallocate its resources. The reallocation was used to improve efficiency and processes of the university in order to better serve its clients.

First Look on Web Mining Techniques to Improve Business Intelligence of E-Commerce Applications

2017 ◽  
pp. 298-314 ◽  
Author(s):  
G. Sreedhar ◽  
A. Anandaraja Chari
Keyword(s):  
Data Mining ◽  
Web Mining ◽  
Web Content ◽  
Web Data ◽  
Web Data Mining ◽  
Useful Knowledge ◽  
Web Usage ◽  
Web Structure ◽  
Web Structure Mining ◽  
Content Mining

Web Data Mining is the application of data mining techniques to extract useful knowledge from web data like contents of web, hyperlinks of documents and web usage logs. There is also a strong requirement of techniques to help in business decision in e-commerce. Web Data Mining can be broadly divided into three categories: Web content mining, Web structure mining and Web usage mining. Web content data are content availed to users to satisfy their required information. Web structure data represents linkage and relationship of web pages to others. Web usage data involves log data collected by web server and application server which is the main source of data. The growth of WWW and technologies has made business functions to be executed fast and easier. As large amount of transactions are performed through e-commerce sites and the huge amount of data is stored, valuable knowledge can be obtained by applying the Web Mining techniques.

Building User Communities of Interests by Using Latent Semantic Analysis

2011 ◽  
pp. 38-68
Author(s):  
Guandong Xu
Keyword(s):  
Latent Semantic Analysis ◽  
Web Mining ◽  
Semantic Analysis ◽  
Information Overload ◽  
Critical Issue ◽  
Web Usage Mining ◽  
Semantic Level ◽  
Web Usage ◽  
User Communities ◽  
Usage Patterns

Nowadays Web users are facing the problems of information overload and drowning due to the significant and rapid growth in the amount of information and the large number of users. As a result, how to provide Web users more exactly needed information is becoming a critical issue in Web-based information retrieval and data management. In order to address the above difficulties, Web mining was proposed as an efficient means to discover the intrinsic relationships among Web data. In particular, Web usage mining is to discover Web usage patterns and utilize the discovered usage knowledge for constructing interest-oriented user communities, which could be, in turn, used for presenting Web users more personalized Web contents, i.e. Web recommendation. On the other hand, Latent Semantic Analysis (LSA) is one kind of approaches that is used to reveal the inherent correlation resided in co-occurrence activities, such as Web usage data. Moreover, LSA possesses the capability of capturing the hidden knowledge at semantic level that can’t be achieved by traditional methods. In this chapter, we aim to address building user communities of interests via combining Web usage mining and latent semantic analysis. Meanwhile we also present the application of user communities for Web recommendation.

A Movie E-Shop Recommendation Model Based on Web Usage and Ontological Data

2011 ◽  
pp. 51-68
Author(s):  
Andreas Aresti ◽  
Penelope Markellou ◽  
Ioanna Mousourouli ◽  
Spiros Sirmakessis ◽  
Athanasios Tsakalidis
Keyword(s):  
Artificial Intelligence ◽  
User Modeling ◽  
Web Mining ◽  
Recommendation System ◽  
Recommendation Systems ◽  
Model Based ◽  
Web Usage ◽  
One To One ◽  
Online Shops

Recommendation systems are special personalization tools that help users to find interesting information and services in complex online shops. Even though today’s e-commerce environments have drastically evolved and now incorporate techniques from other domains and application areas such as Web mining, semantics, artificial intelligence, user modeling, and profiling setting up a successful recommendation system is not a trivial or straightforward task. This chapter argues that by monitoring, analyzing, and understanding the behavior of customers, their demographics, opinions, preferences, and history, as well as taking into consideration the specific e-shop ontology and by applying Web mining techniques, the effectiveness of produced recommendations can be significantly improved. In this way, the e-shop may upgrade users’ interaction, increase its usability, convert users to buyers, retain current customers, and establish long-term and loyal one-to-one relationships.

Web usage analysis Pillar 3 information assessment in turbulent times

2021 ◽  
pp. 157-180
Author(s):  
Anna Pilkova ◽  
Michal Munk ◽  
Petra Blazekova ◽  
Lubomir Benko
Keyword(s):  
Web Usage ◽  
Turbulent Times ◽  
Usage Analysis

Methodologies and Techniques of Web Usage Mining

2017 ◽  
pp. 275-296
Author(s):  
T. Venkat Narayana Rao ◽  
D. Hiranmayi
Keyword(s):  
Web Mining ◽  
Pattern Analysis ◽  
Pattern Discovery ◽  
Secondary Data ◽  
Web Usage Mining ◽  
Sequential Patterns ◽  
Useful Knowledge ◽  
Web Usage ◽  
Automatic Discovery ◽  
Collection Data

Web usage mining attempts to discover useful knowledge from the secondary data obtained from the interactions of the users with the Web. It is the type of Web mining activity that involves the automatic discovery of out what users are looking for on the Internet. In this chapter methodology of web usage mining explained in detail which are data collection, data preprocessing, knowledge discovery and pattern analysis. The different Web Usage Mining techniques are described, which are used for knowledge and pattern discovery. These are statistical analysis, sequential patterns, classification, association rule mining, clustering, dependency modeling. Pattern analysis is needed to filter out uninterested rules or patterns from the set found in the pattern discovery phase.

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