Web Mining System for Mobile-Phone Marketing

Web Mining System for Mobile-Phone Marketing

Miao-Ling Wang (Minghsin University of Science & Technology, Taiwan, ROC) and Hsiao-Fan Wang (National Tsing Hua University, Taiwan, ROC)
Copyright: © 2009 |Pages: 17
DOI: 10.4018/978-1-59904-990-8.ch046
OnDemand PDF Download:
$37.50

Abstract

With the ever-increasing and ever-changing flow of information available on the Web, information analysis has never been more important. Web text mining, which includes text categorization, text clustering, association analysis and prediction of trends, can assist us in discovering useful information in an effective and efficient manner. In this chapter, we have proposed a Web mining system that incorporates both online efficiency and off-line effectiveness to provide the “right” information based on users’ preferences. A Bi-Objective Fuzzy c-Means algorithm and information retrieval technique, for text categorization, clustering and integration, was employed for analysis. The proposed system is illustrated via a case involving the Web site marketing of mobile phones. A variety of Web sites exist on the Internet and a common type involves the trading of goods. In this type of Web site, the question to ask is: If we want to establish a Web site that provides information about products, how can we respond quickly and accurately to queries? This is equivalent to asking: How can we design a flexible search engine according to users’ preferences? In this study, we have applied data mining techniques to cope with such problems, by proposing, as an example, a Web site providing information on mobile phones in Taiwan. In order to efficiently provide useful information, two tasks were considered during the Web design phase. One related to off-line analysis: this was done by first carrying out a survey of frequent Web users, students between 15 and 40 years of age, regarding their preferences, so that Web customers’ behavior could be characterized. Then the survey data, as well as the products offered, were classified into different demand and preference groups. The other task was related to online query: this was done through the application of an information retrieval technique that responded to users’ queries. Based on the ideas above the remainder of the chapter is organized as follows: first, we present a literature review, introduce some concepts and review existing methods relevant to our study, then, the proposed Web mining system is presented, a case study of a mobile-phone marketing Web site is illustrated and finally, a summary and conclusions are offered.
Chapter Preview
Top

Literature Review

Over 150 million people, worldwide, have become Internet users since 1994. The rapid development of information technology and the Internet has changed the traditional business environment. The Internet has enabled the development of Electronic Commerce (e-commerce), which can be defined as selling, buying, conducting logistics, or other organization-management activities, via the Web (Schneider, 2004). Companies are finding that using the Web makes it easier for their business to communicate effectively with customers. For example, Amazom.com, an online bookstore that started up in 1998, reached an annual sales volume of over $1 billion in 2003 (Schneider, 2004). Much research has focused on the impact and mechanisms of e-commerce (Angelides, 1997; Hanson, 2000; Janal, 1995; Mohammed, Fisher, Jaworski, & Paddison, 2004; Rayport & Jaworski, 2002; Schneider, 2004). Although many people challenge the future of e-commerce, Web site managers must take advantage of Internet specialties which potentially enable their companies to make higher profits and their customers to make better decisions. Given that the amount of information available on the Web is large and rapidly increasing, determining an effective way to help users find useful information has become critical. Existing document retrieval systems are mostly based on the Boolean Logic model. Such systems’ applications can be rather limited because they cannot handle ambiguous requests. Chen and Wang (1995) proposed a knowledge-based fuzzy information retrieval method, using the concept of fuzzy sets to represent the categories or features of documents. Fuzzy Set Theory was introduced by Zadeh (1965), and is different from traditional Set Theory, as it uses the concept of membership functions to deal with questions that cannot be solved by two-valued logic. Fuzzy Set Theory concepts have been applied to solve special dynamic processes, especially those observations concerned with linguistic values.

Because the Fuzzy concept has been shown to be applicable when coping with linguistic and vague queries, Chen and Wang’s method is discussed below. Their method is based on a concept matrix for knowledge representation and is defined by a symmetric relation matrix as follows:

(1) where n is the number of concepts, and aij represents the relevant values between concepts Ai and Aj with aii = 1, ∀ i. It can be seen that this concept matrix can reveal the relationship between properties used to describe objects, which has benefits for product identification, query solving, and online sales development. For effective analysis, these properties, determined as the attributes of an object, should be independent of each other; however this may not always be so. Therefore a transitive closure matrix A* must be obtained from the following definition.

Definition 1: Let A be a concept matrix as shown in Equation (1), define:

(2) where ⊗ is the max-min composite operation with “∨” being the maximum operation and “∧” being the minimum operation. If there exists an integer pn – 1 such that Ap = Ap+1 = Ap+2 = ..., A* = Ap is called the Transitive Closure of the concept matrix A.

Matrix A* is an equivalent matrix which satisfies reflexive, symmetric and transitive properties.

To identify each object by its properties, a document descriptor matrix D is constructed in the following form:

(3) where dij represents the degree of relevance of document Di with respect to concept Aj and m is the number of documents in general terms. By applying the max-min composite operation ⊗ to D and A*, we have matrix B = DA*= [bij]m×n where bij represents the relevance of each document Di with respect to a particular concept Aj.

Complete Chapter List

Search this Book:
Reset
Editorial Advisory Board
Table of Contents
Foreword
Xiaohua Hu
Preface
Min Song, Yi-Fang Brook Wu
Acknowledgment
Min Song, Yi-Fang Brook Wu
Chapter 1
Ying Liu
In the automated text classification, a bag-of-words representation followed by the tfidf weighting is the most popular approach to convert the... Sample PDF
On Document Representation and Term Weights in Text Classification
$37.50
Chapter 2
Yi-fang Brook Wu, Quanzhi Li
Document keyphrases provide semantic metadata which can characterize documents and produce an overview of the content of a document. This chapter... Sample PDF
Deriving Document Keyphrases for Text Mining
$37.50
Chapter 3
John Atkinson
This chapter introduces a novel evolutionary model for intelligent text mining. The model deals with issues concerning shallow text representation... Sample PDF
Intelligent Text Mining: Putting Evolutionary Methods and Language Technologies Together
$37.50
Chapter 4
Xiaoyan Yu, Manas Tungare, Weigo Yuan, Yubo Yuan, Manuel Pérez-Quiñones, Edward A. Fox
Syllabi are important educational resources. Gathering syllabi that are freely available and creating useful services on top of the collection... Sample PDF
Automatic Syllabus Classification Using Support Vector Machines
$37.50
Chapter 5
Xiao-Li Li
In traditional text categorization, a classifier is built using labeled training documents from a set of predefined classes. This chapter studies a... Sample PDF
Partially Supervised Text Categorization
$37.50
Chapter 6
Yu-Jin Zhang
Mining techniques can play an important role in automatic image classification and content-based retrieval. A novel method for image classification... Sample PDF
Image Classification and Retrieval with Mining Technologies
$37.50
Chapter 7
Han-joon Kim
This chapter introduces two practical techniques for improving Naïve Bayes text classifiers that are widely used for text classification. The Naïve... Sample PDF
Improving Techniques for Naïve Bayes Text Classifiers
$37.50
Chapter 8
Ricco Rakotomalala, Faouzi Mhamdi
In this chapter, we are interested in proteins classification starting from their primary structures. The goal is to automatically affect proteins... Sample PDF
Using the Text Categorization Framework for Protein Classification
$37.50
Chapter 9
Wilson Wong
Feature-based semantic measurements have played a dominant role in conventional data clustering algorithms for many existing applications. However... Sample PDF
Featureless Data Clustering
$37.50
Chapter 10
Xiaohui Cui
In this chapter, we introduce three nature inspired swarm intelligence clustering approaches for document clustering analysis. The major challenge... Sample PDF
Swarm Intelligence in Text Document Clustering
$37.50
Chapter 11
P. Viswanth
Clustering is a process of finding natural grouping present in a dataset. Various clustering methods are proposed to work with various types of... Sample PDF
Some Efficient and Fast Approaches to Document Clustering
$37.50
Chapter 12
Abdelmalek Amine, Zakaria Elberrichi, Michel Simonet, Ladjel Bellatreche, Mimoun Malki
The classification of textual documents has been the subject of many studies. Technologies like the Web and numerical libraries facilitated the... Sample PDF
SOM-Based Clustering of Textual Documents Using WordNet
$37.50
Chapter 13
Lean Yu, Shouyang Wang, Kin Keung Lai
With the rapid increase of the huge amount of online information, there is a strong demand for Web text mining which helps people discover some... Sample PDF
A Multi-Agent Neural Network System for Web Text Mining
$37.50
Chapter 14
Sangeetha Kutty
With the emergence of XML standardization, XML documents have been widely used and accepted in almost all the major industries. As a result of the... Sample PDF
Frequent Mining on XML Documents
$37.50
Chapter 15
Richi Nayak
XML has gained popularity for information representation, exchange and retrieval. As XML material becomes more abundant, its heterogeneity and... Sample PDF
The Process and Application of XML Data Mining
$37.50
Chapter 16
Francesco Buccafurri
In the context of Knowledge Discovery in Databases, data reduction is a pre-processing step delivering succinct yet meaningful data to sequent... Sample PDF
Approximate Range Querying over Sliding Windows
$37.50
Chapter 17
Jan H. Kroeze
This chapter discusses the application of some data warehousing techniques on a data cube of linguistic data. The results of various modules of... Sample PDF
Slicing and Dicing a Linguistic Data Cube
$37.50
Chapter 18
Yi-fang Brook Wu, Xin Chen
This chapter presents a methodology for personalized knowledge discovery from text. Traditionally, problems with text mining are numerous rules... Sample PDF
Discovering Personalized Novel Knowledge from Text
$37.50
Chapter 19
Catia Pesquita
Biomedical research generates a vast amount of information that is ultimately stored in scientific publications or in databases. The information in... Sample PDF
Untangling BioOntologies for Mining Biomedical Information
$37.50
Chapter 20
Luis M. de Campos
In this chapter, we present a thesaurus application in the field of text mining and more specifically automatic indexing on the set of descriptors... Sample PDF
Thesaurus-Based Automatic Indexing
$37.50
Chapter 21
Concept-Based Text Mining  (pages 346-358)
Stanley Loh, Leandro Krug Wives, Daniel Lichtnow, José Palazzo M. de Oliveira
The goal of this chapter is to present an approach to mine texts through the analysis of higher level characteristics (called “concepts’)... Sample PDF
Concept-Based Text Mining
$37.50
Chapter 22
Marcello Pecoraro
This chapter aims at providing an overview about the use of statistical methods supporting the Web Usage Mining. Within the first part is described... Sample PDF
Statistical Methods for User Profiling in Web Usage Mining
$37.50
Chapter 23
Quanzhi Li, Yi-fang Brook Wu
This chapter presents a new approach of mining the Web to identify people of similar background. To find similar people from the Web for a given... Sample PDF
Web Mining to Identify People of Similar Background
$37.50
Chapter 24
Pawan Lingras
This chapter describes how Web usage patterns can be used to improve the navigational structure of a Web site. The discussion begins with an... Sample PDF
Hyperlink Structure Inspired by Web Usage
$37.50
Chapter 25
Rosa Meo, Maristella Matera
In this chapter, we present the usage of a modeling language, WebML, for the design and the management of dynamic Web applications. WebML also makes... Sample PDF
Designing and Mining Web Applications: A Conceptual Modeling Approach
$37.50
Chapter 26
Brigitte Trousse, Marie-Aude Aufaure, Bénédicte Le Grand, Yves Lechevallier, Florent Masseglia
This chapter proposes an original approach for ontology management in the context of Web-based information systems. Our approach relies on the usage... Sample PDF
Web Usage Mining for Ontology Management
$37.50
Chapter 27
Yue-Shi Lee
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... Sample PDF
A Lattice-Based Framework for Interactively and Incrementally Mining Web Traversal Patterns
$37.50
Chapter 28
Stanley R.M. Oliveira, Osmar R. Zaïane
Privacy-preserving data mining (PPDM) is one of the newest trends in privacy and security research. It is driven by one of the major policy issues... Sample PDF
Privacy-Preserving Data Mining on the Web: Foundations and Techniques
$37.50
Chapter 29
G.S. Mahalakshmi, S. Sendhilkumar
Automatic reference tracking involves systematic tracking of reference articles listed for a particular research paper by extracting the references... Sample PDF
Automatic Reference Tracking
$37.50
Chapter 30
Wilson Wong
As more electronic text is readily available, and more applications become knowledge intensive and ontology-enabled, term extraction, also known as... Sample PDF
Determination of Unithood and Termhood for Term Recognition
$37.50
Chapter 31
Fotis Lazarinis
Over 60% of the online population are non-English speakers and it is probable the number of non-English speakers is growing faster than English... Sample PDF
Retrieving Non-Latin Information in a Latin Web: The Case of Greek
$37.50
Chapter 32
Anne Kao
Latent Semantic Analysis (LSA) or Latent Semantic Indexing (LSI), when applied to information retrieval, has been a major analysis approach in text... Sample PDF
Latent Semantic Analysis and Beyond
$37.50
Chapter 33
Ganesh Ramakrishnan, Pushpak Bhattacharyya
Text mining systems such as categorizers and query retrievers of the first generation were largely hinged on word level statistics and provided a... Sample PDF
Question Answering Using Word Associations
$37.50
Chapter 34
Giuseppe Manco, Riccardo Ortale, Andrea Tagarelli
Personalization is aimed at adapting content delivery to users’ profiles: namely, their expectations, preferences and requirements. This chapter... Sample PDF
The Scent of a Newsgroup: Providing Personalized Access to Usenet Sites through Web Mining
$37.50
Chapter 35
Alexander Dreweke, Ingrid Fischer, Tobias Werth, Marc Wörlein
Searching for frequent pieces in a database with some sort of text is a well-known problem. A special sort of text is program code as e.g. C++ or... Sample PDF
Text Mining in Program Code
$37.50
Chapter 36
Nitin Agarwal, Huan Liu, Jianping Zhang
In Golbeck and Hendler (2006), authors consider those social friendship networking sites where users explicitly provide trust ratings to other... Sample PDF
A Study of Friendship Networks and Blogosphere
$37.50
Chapter 37
Pasquale De Meo
In this chapter we present an information system conceived for supporting managers of Public Health Care Agencies to decide the new health care... Sample PDF
An HL7-Aware Decision Support System for E-Health
$37.50
Chapter 38
Diego Liberati
Building effective multitarget classifiers is still an on-going research issue: this chapter proposes the use of the knowledge gleaned from a human... Sample PDF
Multitarget Classifiers for Mining in Bioinformatics
$37.50
Chapter 39
Shuting Xu
Text mining is an instrumental technology that today’s organizations can employ to extract information and further evolve and create valuable... Sample PDF
Current Issues and Future Analysis in Text Mining for Information Security Applications
$37.50
Chapter 40
E. Thirumaran
This chapter introduces Collaborative filtering-based recommendation systems, which has become an integral part of E-commerce applications, as can... Sample PDF
Collaborative Filtering Based Recommendation Systems
$37.50
Chapter 41
Hanna Suominen
The purpose of this chapter is to provide an overview of prevalent measures for evaluating the quality of system output in seven key text mining... Sample PDF
Performance Evaluation Measures for Text Mining
$37.50
Chapter 42
Yanliang Qi
The biology literatures have been increased in an exponential growth in recent year. The researchers need an effective tool to help them find out... Sample PDF
Text Mining in Bioinformatics: Research and Application
$37.50
Chapter 43
Ki Jung Lee
With the increased use of Internet, a large number of consumers first consult on line resources for their healthcare decisions. The problem of the... Sample PDF
Literature Review in Computational Linguistics Issues in the Developing Field of Consumer Informatics: Finding the Right Information for Consumer's Health Information Need
$37.50
Chapter 44
Richard S. Segall
This chapter presents background on text mining, and comparisons and summaries of seven selected software for text mining. The text mining software... Sample PDF
A Survey of Selected Software Technologies for Text Mining
$37.50
Chapter 45
Ah Chung Tsoi, Phuong Kim To, Markus Hagenbuchner
This chapter describes the application of a number of text mining techniques to discover patterns in the health insurance schedule with an aim to... Sample PDF
Application of Text Mining Methodologies to Health Insurance Schedules
$37.50
Chapter 46
Miao-Ling Wang, Hsiao-Fan Wang
With the ever-increasing and ever-changing flow of information available on the Web, information analysis has never been more important. Web text... Sample PDF
Web Mining System for Mobile-Phone Marketing
$37.50
Chapter 47
Neil Davis
Text mining technology can be used to assist in finding relevant or novel information in large volumes of unstructured data, such as that which is... Sample PDF
Web Service Architectures for Text Mining: An Exploration of the Issues via an E-Science Demonstrator
$37.50
About the Editors
About the Contributors