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Data Mining and Business Intelligence

Intelligent Agents for Data Mining and Information Retrieval

Data Mining and Knowledge Discovery with Evolutionary Algorithms

Optimization Based Data Mining: Theory and Applications

Data mining and data based direct marketing activities

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Data Mining: Opportunities and Challenges presents an overview of the state of the art approaches in this new and multidisciplinary field of data mining. The primary objective of this book is to explore the myriad issues regarding data mining, specifically focusing on those areas that explore new methodologies or examine case studies. This book contains numerous chapters written by an international team of forty-four experts representing leading scientists and talented young scholars from seven different countries.

John Wang,

This chapter presents an approach to mining free-text documents for structure
that is qualitative in nature. It complements the statistical and machine-learning
approaches, insomuch as the structural organization of information in documents
is ...

Microsoft Data Mining

Integrated Business Intelligence for E-Commerce and Knowledge Management

This guide teaches data mining from the perspective of IT professionals using Microsoft data management and e-commerce technologies. The book explains major new data mining capabilities in the forthcoming SQL Server 2000, Microsoft Commerce Server, and other products, and details the new Microsoft standard, "OLE DB for Data Mining".

Barry De Ville,

Tom DeMarco Pulling data together into an analysis environment — called here
a mining mart — is an essential precondition to providing data in the right form
and providing the right measurements in order to produce a timely and useful ...

Data Mining and Business Intelligence

A Guide to Productivity

Annotation Provides an overview of data mining technology and how it is applied in a business environment. Material is not written in a technical style, but rather addresses the applied methodology behind implementing data mining techniques in the corporate environment. Explains how the technology evolved, overviews the methodologies that comprise the data mining spectrum, and looks at everyday business applications for data mining, in areas such as marketing and advertising promotions and pricing policies using econometric-based modeling, and using the Internet to help improve an organization's performance. Kudyba is an economic consultant. Hoptroff is an independent consultant with experience in data mining software. Annotation c. Book News, Inc., Portland, OR (booknews.com).

Stephan Kudyba, Richard Hoptroff,

At this juncture it is appropriate to offer a more formal definition of data mining
before we progress to a more thorough description of the techniques,
applications and general methodologies of the data mining process in the world
of Business ...

Data Mining Techniques

This Book Addresses All The Major And Latest Techniques Of Data Mining And Data Warehousing. It Deals With The Latest Algorithms For Discussing Association Rules, Decision Trees, Clustering, Neural Networks And Genetic Algorithms. The Book Also Discusses The Mining Of Web Data, Temporal And Text Data. It Can Serve As A Textbook For Students Of Compuer Science, Mathematical Science And Management Science, And Also Be An Excellent Handbook For Researchers In The Area Of Data Mining And Warehousing.

Arun K. Pujari,

This Book Addresses All The Major And Latest Techniques Of Data Mining And Data Warehousing.

Data Mining

Concepts, Methods and Applications in Management and Engineering Design

Data Mining introduces in clear and simple ways how to use existing data mining methods to obtain effective solutions for a variety of management and engineering design problems. Data Mining is organised into two parts: the first provides a focused introduction to data mining and the second goes into greater depth on subjects such as customer analysis. It covers almost all managerial activities of a company, including: • supply chain design, • product development, • manufacturing system design, • product quality control, and • preservation of privacy. Incorporating recent developments of data mining that have made it possible to deal with management and engineering design problems with greater efficiency and efficacy, Data Mining presents a number of state-of-the-art topics. It will be an informative source of information for researchers, but will also be a useful reference work for industrial and managerial practitioners.

Yong Yin, Ikou Kaku, Jiafu Tang, JianMing Zhu,

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 of the
information era: the right to privacy. Data mining is the process of automatically
discovering ...

Intelligent Agents for Data Mining and Information Retrieval

There is a large increase in the amount of information available on World Wide Web and also in number of online databases. This information abundance increases the complexity of locating relevant information. Such a complexity drives the need for improved and intelligent systems for search and information retrieval. Intelligent agents are currently used to improve the search and retrieval information on World Wide Web. The use of existing search and retrieval engines with the addition of intelligent agents allows a more comprehensive search with a performance that can be measured. Intelligent Agents for Data Mining and Information Retrieval discusses the foundation as well as the practical side of intelligent agents and their theory and applications for web data mining and information retrieval. The book can used for researchers at the undergraduate and post-graduate levels as well as a reference of the state-of-art for cutting edge researchers.

Masoud Mohammadian,

The book can used for researchers at the undergraduate and post-graduate levels as well as a reference of the state-of-art for cutting edge researchers.

Data Mining and Knowledge Discovery with Evolutionary Algorithms

This book integrates two areas of computer science, namely data mining and evolutionary algorithms. Both these areas have become increasingly popular in the last few years, and their integration is currently an area of active research. In general, data mining consists of extracting knowledge from data. In this book we particularly emphasize the importance of discovering comprehensible and interesting knowledge, which is potentially useful to the reader for intelligent decision making. In a nutshell, the motivation for applying evolutionary algorithms to data mining is that evolutionary algorithms are robust search methods which perform a global search in the space of candidate solutions (rules or another form of knowledge representation). In contrast, most rule induction methods perform a local, greedy search in the space of candidate rules. Intuitively, the global search of evolutionary algorithms can discover interesting rules and patterns that would be missed by the greedy search. This book presents a comprehensive review of basic concepts on both data mining and evolutionary algorithms and discusses significant advances in the integration of these two areas. It is self-contained, explaining both basic concepts and advanced topics.

Alex A. Freitas,

This book integrates two areas of computer science, namely data mining and evolutionary algorithms.

Journeys to Data Mining

Experiences from 15 Renowned Researchers

Data mining, an interdisciplinary field combining methods from artificial intelligence, machine learning, statistics and database systems, has grown tremendously over the last 20 years and produced core results for applications like business intelligence, spatio-temporal data analysis, bioinformatics, and stream data processing. The fifteen contributors to this volume are successful and well-known data mining scientists and professionals. Although by no means an exhaustive list, all of them have helped the field to gain the reputation and importance it enjoys today, through the many valuable contributions they have made. Mohamed Medhat Gaber has asked them (and many others) to write down their journeys through the data mining field, trying to answer the following questions: 1. What are your motives for conducting research in the data mining field? 2. Describe the milestones of your research in this field. 3. What are your notable success stories? 4. How did you learn from your failures? 5. Have you encountered unexpected results? 6. What are the current research issues and challenges in your area? 7. Describe your research tools and techniques. 8. How would you advise a young researcher to make an impact? 9. What do you predict for the next two years in your area? 10. What are your expectations in the long term? In order to maintain the informal character of their contributions, they were given complete freedom as to how to organize their answers. This narrative presentation style provides PhD students and novices who are eager to find their way to successful research in data mining with valuable insights into career planning. In addition, everyone else interested in the history of computer science may be surprised about the stunning successes and possible failures computer science careers (still) have to offer.

Mohamed Medhat Gaber,

The fifteen contributors to this volume are successful and well-known data mining scientists and professionals.

Optimization Based Data Mining: Theory and Applications

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining. Optimization based Data Mining: Theory and Applications, mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery. Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

Yong Shi, Yingjie Tian, Gang Kou, Yi Peng, Jianping Li,

Most of the material in this book is directly from the research and application activities that the authors’ research group has conducted over the last ten years.

Data Mining

Technologies, Techniques, Tools, and Trends

Focusing on a data-centric perspective, this book provides a complete overview of data mining: its uses, methods, current technologies, commercial products, and future challenges. Three parts divide Data Mining: Part I describes technologies for data mining - database systems, warehousing, machine learning, visualization, decision support, statistics, parallel processing, and architectural support for data mining Part II presents tools and techniques - getting the data ready, carrying out the mining, pruning the results, evaluating outcomes, defining specific approaches, examining a specific technique based on logic programming, and citing literature and vendors for up-to-date information Part III examines emerging trends - mining distributed and heterogeneous data sources; multimedia data, such as text, images, video; mining data on the World Wide Web; metadata aspects of mining; and privacy issues. This self-contained book also contains two appendices providing exceptional information on technologies, such as data management, and artificial intelligence. Is there a need for mining? Do you have the right tools? Do you have the people to do the work? Do you have sufficient funds allocated to the project? All these answers must be answered before embarking on a project. Data Mining provides singular guidance on appropriate applications for specific techniques as well as thoroughly assesses valuable product information.

Bhavani Thuraisingham,

CHAPTER 1 INTRODUCTION 1.1 WHAT IS DATA MINING? Data mining is the
process of posing various queries and extracting useful information, patterns, and
trends often previously unknown from large quantities of data possibly stored in ...

Educational Data Mining

Applications and Trends

This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research. After a rigorous revision process sixteen manuscripts were accepted and organized into four parts as follows: · Profile: The first part embraces three chapters oriented to: 1) describe the nature of educational data mining (EDM); 2) describe how to pre-process raw data to facilitate data mining (DM); 3) explain how EDM supports government policies to enhance education. · Student modeling: The second part contains five chapters concerned with: 4) explore the factors having an impact on the student's academic success; 5) detect student's personality and behaviors in an educational game; 6) predict students performance to adjust content and strategies; 7) identify students who will most benefit from tutor support; 8) hypothesize the student answer correctness based on eye metrics and mouse click. · Assessment: The third part has four chapters related to: 9) analyze the coherence of student research proposals; 10) automatically generate tests based on competences; 11) recognize students activities and visualize these activities for being presented to teachers; 12) find the most dependent test items in students response data. · Trends: The fourth part encompasses four chapters about how to: 13) mine text for assessing students productions and supporting teachers; 14) scan student comments by statistical and text mining techniques; 15) sketch a social network analysis (SNA) to discover student behavior profiles and depict models about their collaboration; 16) evaluate the structure of interactions between the students in social networks. This volume will be a source of interest to researchers, practitioners, professors, and postgraduate students aimed at updating their knowledge and find targets for future work in the field of educational data mining.

Alejandro Peña-Ayala,

This book is devoted to the Educational Data Mining arena. It highlights works that show relevant proposals, developments, and achievements that shape trends and inspire future research.

Data mining and data based direct marketing activities

Master's Thesis from the year 2004 in the subject Business economics - Marketing, Corporate Communication, CRM, Market Research, Social Media, grade: 1,7 (A-), Växjö University (School of Management and Economics), course: International Business Environment, 40 entries in the bibliography, language: English, abstract: Widespread changes within business environments in recent years has demanded acquisitions of new tools that are more skilled to cope with new challenges and demands in business. Advances in computer technologies, higher accessibility of computer associated tools and decreased prices of general computer-related products are reasons enough for at least considerations about higher usage of new technologies. Particularly in direct marketing activities discussed technology is called Data Mining. Companies are faced with hosts of data collected in their data repositories. Of course, companies want to make use of their data and aim to discover interesting patterns of knowledge within their data repositories. Direct marketers which can be involved in catalogue marketing, telemarketing or widely known direct-mail marketing are intensive users of Data Mining Technologies. Because of that, the authors strive to do research concerning reasons for and advantages and disadvantages with using Data Mining as support for direct marketing activities. Chapter 1 deals with general information for the reader as support for delving into the topic. The included problem discussion finishes with the final problem formulation of this thesis. Chapter 2 is about the Methodology which includes considerations of Gummesson. The following theoretical part is divided into two major parts, Data Mining and Direct Marketing, which underpin the whole thesis. The authors want to inform the reader about important and sophisticated contents concerning both Data Mining and Direct Marketing. Without overloading the implementations about Data Mining and Direct Marketing, the authors conduct the reader to essential and detailed aspects of both fields for understanding the intentions. The empirical part contains a short introduction to each company within the thesis, and short summaries of the interviews conducted. In the following analysis part the authors have created a model to make the possible uses of Data Mining more understandable to the reader. Furthermore, this part contains an analysis of the interviews in relation to the topic at hand and the theories used. In the conclusions the authors answer their research question, namely; what are the main advantages and disadvantages of Data Mining as support to Direct Marketing activities? In the absolute end of the thesis the authors criticise their own work and give suggestions for further research within the topic.

T. Brüggemann, P. Hedström, M. Josefsson,

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