Data mining is already incorporated into the business processes in many sectors such as health, retail, automotive, finance, telecom and insurance as well as in government. This technology is well established in applications such as targeted marketing, customer churn detection and market basket analysis. It is also emerging as an important technology in a wide range of new application areas, such as social media, social networks and sensor networks. These areas pose new challenges both in terms of the nature of available data and the underlying support technology. This book contains extended versions of a selection of papers presented at a series of workshops held between 2005 and 2008 on the subject of data mining for business applications. It covers the entire spectrum of issues involved in the development of data mining systems. Areas covered include methodological issues and research challenges, typical problems for which data mining has proved to be an invaluable tool, and innovative applications of data mining which make this an exciting field to work in. The contributions illustrate the importance of maintaining close contact between researchers and practitioners: it is essential that researchers are exposed to and motivated by the real problems and practical constraints experienced by organizations, and practitioners need to interact with the research community to identify new opportunities to apply the latest technology. This book will be of interest not only to data mining researchers and practitioners, but also to students seeking a better understanding of the practical issues involved in building data mining systems.
This book contains extended versions of a selection of papers presented at a series of workshops held between 2005 and 2008 on the subject of data mining for business applications.
Decisions regarding the risks involved in medical treatments must belong to patients and their physicians – after all, it is the patient's health and life which is at stake. But patients will not be equipped for this decision-making process if they cannot be given some idea as to the risks and benefits of treatment. Such risks are generally estimated by a consensus panel of specialist physicians using supporting medical literature. Unfortunately, this literature does not always provide a good estimate of risk, particularly in the case of rare occurrences. This book demonstrates statistical techniques that can be used to investigate matters of risk. These include kernel density estimation, predictive modeling, association rules and text analysis. It also shows, through example, how these techniques can provide meaningful results, and examines current methods, discussing some of the flaws in models which may lead to misleading results. After a general introduction to the concept of medical risk, the subjects covered include the process by which rare occurrences are investigated in drugs or treatments, the trade-offs between risks and benefits, extrapolation of clinical trial results and the cost of healthcare in relation to risks. It also examines problems such as competing risks, error, and the use of group identities, as well as looking at the issue of futility. The book concludes with a chapter providing a general discussion and summary, and an appendix shows some of the processes for using SAS Enterprise Miner to perform some of the models used in the text.
Unfortunately, this literature does not always provide a good estimate of risk, particularly in the case of rare occurrences. This book demonstrates statistical techniques that can be used to investigate matters of risk.
The real power for security applications will come from the synergy of academic and commercial research focusing on the specific issue of security. Special constraints apply to this domain, which are not always taken into consideration by academic research, but are critical for successful security applications: large volumes: techniques must be able to handle huge amounts of data and perform 'on-line' computation; scalability: algorithms must have processing times that scale well with ever growing volumes; automation: the analysis process must be automated so that information extraction can 'run on its own'; ease of use: everyday citizens should be able to extract and assess the necessary information; and robustness: systems must be able to cope with data of poor quality (missing or erroneous data). The NATO Advanced Study Institute (ASI) on Mining Massive Data Sets for Security, held in Italy, September 2007, brought together around ninety participants to discuss these issues. This publication includes the most important contributions, but can of course not entirely reflect the lively interactions which allowed the participants to exchange their views and share their experience. The bridge between academic methods and industrial constraints is systematically discussed throughout. This volume will thus serve as a reference book for anyone interested in understanding the techniques for handling very large data sets and how to apply them in conjunction for solving security issues.
This volume will thus serve as a reference book for anyone interested in understanding the techniques for handling very large data sets and how to apply them in conjunction for solving security issues.
With the increased possibilities in modern society for companies and institutions to gather data cheaply and efficiently, the subject of Data Mining has become of increasing importance. This interest has inspired a rapidly maturing research field with developments both on a theoretical, as well as on a practical level with the availability of a range of commercial tools. Unfortunately, the widespread application of this technology has been limited by an important assumption in mainstream Data Mining approaches. This assumption – all data resides, or can be made to reside, in a single table – prevents the use of these Data Mining tools in certain important domains, or requires considerable massaging and altering of the data as a pre-processing step. This limitation has spawned a relatively recent interest in richer Data Mining paradigms that do allow structured data as opposed to the traditional flat representation. This publication goes into the different uses of Data Mining, with Multi-Relational Data Mining (MRDM), the approach to Structured Data Mining, as the main subject of this book.
This thesis is concerned with Data Mining: extracting useful insights from large
and detailed collections of data. With the increased possibilities in modern
society for companies and institutions to gather data cheaply and efficiently, this
subject ...
The application of Data Mining (DM) technologies has shown an explosive growth in an increasing number of different areas of business, government and science. Two of the most important business areas are finance, in particular in banks and insurance companies, and e-business, such as web portals, e-commerce and ad management services.In spite of the close relationship between research and practice in Data Mining, it is not easy to find information on some of the most important issues involved in real world application of DM technology, from business and data understanding to evaluation and deployment. Papers often describe research that was developed without taking into account constraints imposed by the motivating application. When these issues are taken into account, they are frequently not discussed in detail because the paper must focus on the method. Therefore knowledge that could be useful for those who would like to apply the same approach on a related problem is not shared. The papers in this book address some of these issues. This book is of interest not only to Data Mining researchers and practitioners, but also to students who wish to have an idea of the practical issues involved in Data Mining.
All rights reserved. doi:10.3233/978-1-58603-890-8-133 Sequence Mining for
Business Analytics: Building Project Taxonomies for Resource Demand
Forecasting Ritendra DATTA a, Jianying HU b and Bonnie RAY b,c aDepartment
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Self-organization and adaptation are concepts stemming from the nature and have been adopted in systems theory. This book provides in-depth thoughts about several methodologies and technologies for the area. It represents the future generation of IT systems, comprised of communication infrastructures and computing applications.
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This book is an introduction into methodology and practice of analysis, design and implementation of distributed health information systems. Special attention is dedicated to security and interoperability of such systems as well as to advanced electronic health record approaches. In the book, both available architectures and implementations but also current and future innovations are considered. Therefore, the component paradigm, UML, XML, eHealth are discussed in a concise way. Many practical solutions specified and implemented first in the author's environment are presented in greater detail. The book addresses information scientists, administrators, health professionals, managers and other users of health information systems.
