Machine Learning and Web Mining: Methods and Applications in Societal Benefit Areas

Machine Learning and Web Mining: Methods and Applications in Societal Benefit Areas

Georgios Lappas (Technological Educational Institution of Western Macedonia, Kastoria Campus, Greece)
Copyright: © 2009 |Pages: 20
DOI: 10.4018/978-1-59904-657-0.ch005
OnDemand PDF Download:


This chapter reviews research on machine learning and Web mining methods that are related to areas of social benefit. It shows that machine learning and Web mining methods may provide intelligent Web services of social interest. The chapter reveals a growing interest for using advanced computational methods, such as machine learning and Web mining, for better services to the public, as most research identified in the literature has been conducted during the last years. The chapter objective is to help researchers and academics from different disciplines to understand how Web mining and machine learning methods are applied to Web data. Furthermore it aims to provide the latest developments on research that is related to societal benefit areas.
Chapter Preview


The Web is constantly becoming a central part of social, cultural, political, educational, academic, and commercial life and contains a wide range of information and applications in areas that are of societal interest. Web mining is the field of data mining that is related to the discovery of knowledge from the Web. The Web can be considered as a tremendously large and rich in content knowledge base of heterogeneous entries without any well specified structure, which proportionally makes the Web at least as complex as any known complex database and perhaps the largest knowledge repository. The vast information that surrounds the Web does not come only from the content of Websites, but is also related to usage of Web pages, navigation paths and networking between the links of Web-pages. All these properties establish the Web as a very challenging area for the machine learning community to apply their methods usually for extracting new knowledge, discovering interesting patterns and enhancing the efficiency of Websites by providing user-demand content and design.

Web mining is a relatively new area, broadly interdisciplinary, attracting researchers from: computer science fields like artificial intelligence, machine learning, databases, and information retrieval specialists; from business studies fields like marketing, administrative and e-commerce specialists; and from social and communication studies fields such as social network analyzers, pedagogical scientists, and political science specialists. Herrera-Viedma and Pasi (2006) denote that due to the complexity of Web research there is a requirement for the use of interdisciplinary approaches like statistics, databases, information retrieval, decision theory, artificial intelligence, cognitive social theory and behavioral science. As a relatively new area there is a lot of confusion when comparing research efforts from different point of views (Kosala & Blockeel, 2000) and therefore there is a need for surveys that record and aggregate efforts done by independent researchers, provide definitions and explain structures and taxonomies of the field from various points of view.

The overall objective of this chapter is to provide a review of different machine learning approaches to Web mining and draw conclusions on their applicability in societal benefit areas. The novelty of this review is that it focuses on Web mining in societal benefit areas. There exist similar work related to Web mining in (Baldi, Frasconi, & Smyth, 2003; Chakrabarti, 2003; Chen & Chau, 2004; Pal, Talwar & Mitra, 2002). Baldi et al. (2003) cover research and theory on aspects of Internet and Web modeling at the information level based on mathematical, probabilistic, and graphical treatment. Chakrabarti focuses on studies that connect users to the information they seek from the Web providing lots of programs with pseudocode. Chen and Chau provide an extended review of how machine-learning techniques for traditional information retrieval systems have been improved and adapted for Web mining applications. Pal et al. (2002) present an overview of machine learning techniques with focusing on a specific Web mining category, the Web content mining that will be described in next section. This work is differentiated from the aforementioned related work as the chapter particularly focuses on Web mining and machine learning that may help and benefit societal areas in ways of extracting new knowledge, providing support for decision making and empowering valuable management of societal issues. This survey aims to help researchers and academics from different disciplines to understand Web mining and machine learning methods. Thus, it is aimed at a relatively broad audience and tries to provide them with a different and more open view on Web research. Therefore this work addresses researchers from both computer science and other than computer science disciplines with the intention: (a) for computer science researchers, to provide them with the latest developments on the theory and applications of Web mining, focusing also to the need for Web mining applications in societal beneficial areas, and (b) for researchers from other than computer science disciplines, to draw their attention to existing machine learning methods that may help them to seek for more effective results in their Web research.

Complete Chapter List

Search this Book:
Table of Contents
Abdul Matin Patwari
Hakikur Rahman
Chapter 1
Yong Shi, Yi Peng, Gang Kou, Zhengxin Chen
This chapter provides an overview of a series of multiple criteria optimization-based data mining methods, which utilize multiple criteria... Sample PDF
Introduction to Data Mining Techniques via Multiple Criteria Optimization Approaches and Applications
Chapter 2
Kevin Swingler, David Cairns
This chapter identifies important barriers to the successful application of computational intelligence (CI) techniques in a commercial environment... Sample PDF
Making Decisions with Data: Using Computational Intelligence Within a Business Environment
Chapter 3
A.V. Senthil Kumar, R. S.D. Wahidabanu
This chapter describes two techniques used to explore frequent large itemsets in the database. In the first technique called “closed directed graph... Sample PDF
Data Mining Association Rules for Making Knowledgeable Decisions
Chapter 4
Marcelino Pereira dos Santos Silva, Gilberto Câmara, Maria Isabel Sobral Escada
Daily, different satellites capture data of distinct contexts, which images are processed and stored in many institutions. This chapter presents... Sample PDF
Image Mining: Detecting Deforestation Patterns Through Satellites
Chapter 5
Georgios Lappas
This chapter reviews research on machine learning and Web mining methods that are related to areas of social benefit. It shows that machine learning... Sample PDF
Machine Learning and Web Mining: Methods and Applications in Societal Benefit Areas
Chapter 6
Diana Luck
In recent times, customer relationship management (CRM) has been defined as relating to sales, marketing, and even services automation.... Sample PDF
The Importance of Data Within Contemporary CRM
Chapter 7
Yanbo J. Wang, Xinwei Zheng, Frans Coenen
An association rule (AR) is a common type of mined knowledge in data mining that describes an implicative co-occurring relationship between two sets... Sample PDF
Mining Allocating Patterns in Investment Portfolios
Chapter 8
Hakikur Rahman
Social development activities are flourishing in diversified branches of society endeavor, despite numerous hurdles inflicting on their ways that... Sample PDF
Application of Data Mining Algorithms for Measuring Performance Impact of Social Development Activities
Chapter 9
Hakikur Rahman
Society development activities are continuous processes that are intended to uplift the livelihood of communities and thereby empower the members of... Sample PDF
Prospects and Scopes of Data Mining Applications in Society Development Activities
Chapter 10
Indranil Bose, Lam Albert Kar Chun, Leung Vivien Wai Yue, Li Hoi Wan Ines, Wong Oi Ling Helen
The retailing giant Wal-Mart owes its success to the efficient use of information technology in its operations. One of the noteworthy advances made... Sample PDF
Business Data Warehouse: The Case of Wal-Mart
Chapter 11
Ronald N. Kostoff, Raymond G. Koytcheff, Clifford G.Y. Lau
The medical applications literature associated with nanoscience and nanotechnology research was examined. About 65,000 nanotechnology records for... Sample PDF
Medical Applications of Nanotechnology in the Research Literature
Chapter 12
Ali Serhan Koyuncugil
This chapter introduces an early warning system for SMEs (SEWS) as a financial risk detector which is based on data mining. In this study, the... Sample PDF
Early Warning System for SMEs as a Financial Risk Detector
Chapter 13
Maira Petrini, Marlei Pozzebon
Constant technological innovation and increasing competitiveness make the management of information a considerable challenge, requiring... Sample PDF
What Role is "Business Intelligence" Playing in Developing Countries? A Picture of Brazilian Companies
Chapter 14
Inya Nlenanya
Technologies such as geographic information systems (GIS) enable geospatial information to be captured, updated, integrated, and mapped easily and... Sample PDF
Building an Environmental GIS Knowledge Infrastructure
Chapter 15
Tsegaye Tadesse, Brian Wardlow, Michael J. Hayes
This chapter discusses the application of data mining to develop drought monitoring tools that enable monitoring and prediction of drought’s impact... Sample PDF
The Application of Data Mining for Drought Monitoring and Prediction
About the Contributors