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Feature Selection for Knowledge Discovery and Data Mining

With advanced computer technologies and their omnipresent usage, data accumulates in a speed unmatchable by the human's capacity to process data. To meet this growing challenge, the research community of knowledge discovery from databases emerged. The key issue studied by this community is, in layman's terms, to make advantageous use of large stores of data. In order to make raw data useful, it is necessary to represent, process, and extract knowledge for various applications. Feature Selection for Knowledge Discovery and Data Mining offers an overview of the methods developed since the 1970s and provides a general framework in order to examine these methods and categorize them. This book employs simple examples to show the essence of representative feature selection methods and compares them using data sets with combinations of intrinsic properties according to the objective of feature selection. In addition, the book suggests guidelines on how to use different methods under various circumstances and points out new challenges in this exciting area of research. Feature Selection for Knowledge Discovery and Data Mining is intended to be used by researchers in machine learning, data mining, knowledge discovery and databases as a toolbox of relevant tools that help in solving large real-world problems. This book is also intended to serve as a reference book or secondary text for courses on machine learning, data mining, and databases.

As computer power grows and data collection technologies advance, a plethora of data is generated in almost every field where computers are used.

Contrast Data Mining

Concepts, Algorithms, and Applications

A Fruitful Field for Researching Data Mining Methodology and for Solving Real-Life Problems Contrast Data Mining: Concepts, Algorithms, and Applications collects recent results from this specialized area of data mining that have previously been scattered in the literature, making them more accessible to researchers and developers in data mining and other fields. The book not only presents concepts and techniques for contrast data mining, but also explores the use of contrast mining to solve challenging problems in various scientific, medical, and business domains. Learn from Real Case Studies of Contrast Mining Applications In this volume, researchers from around the world specializing in architecture engineering, bioinformatics, computer science, medicine, and systems engineering focus on the mining and use of contrast patterns. They demonstrate many useful and powerful capabilities of a variety of contrast mining techniques and algorithms, including tree-based structures, zero-suppressed binary decision diagrams, data cube representations, and clustering algorithms. They also examine how contrast mining is used in leukemia characterization, discriminative gene transfer and microarray analysis, computational toxicology, spatial and image data classification, voting analysis, heart disease prediction, crime analysis, understanding customer behavior, genetic algorithms, and network security.

Contrast data mining is an important and focused subarea of data mining. Its aim
is to find interesting contrast patterns that describe significant differences
between datasets satisfying various contrasting conditions. The contrasting
conditions ...

Knowledge Discovery and Data Mining

Knowledge discovery has been defined as "the extraction of implicit, previously unknown and potentially useful information from data." In a world increasingly overloaded with data of varying quality, not least via the Internet, computerized tools are becoming useful to "mine" useful data from the mass available. This has led to data mining becoming an important aspect of IT and applied computing. This book reviews some of the underlying technologies and also some recent applications in a number of fields.

premining data mining DT=dimension table Figure 6.2 The feedback sandwich
model The top layer of this model indicates premining of data. The filling part of
the sandwich is the combined OLAP/data-mining engine, which supports ...

Scalable High Performance Computing for Knowledge Discovery and Data Mining

Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area. Scalable High Performance Computing for Knowledge Discovery and Data Mining serves as an excellent reference, providing insight into some of the most challenging research issues in the field.

Scalable High Performance Computing for Knowledge Discovery and Data Mining brings together in one place important contributions and up-to-date research results in this fast moving area.

Web Data Mining and Applications in Business Intelligence and Counter-Terrorism

The explosion of Web-based data has created a demand among executives and technologists for methods to identify, gather, analyze, and utilize data that may be of value to corporations and organizations. The emergence of data mining, and the larger field of Web mining, has businesses lost within a confusing maze of mechanisms and strategies for obtaining and managing crucial intelligence. Web Data Mining and Applications in Business Intelligence and Counter-Terrorism responds by presenting a clear and comprehensive overview of Web mining, with emphasis on CRM and, for the first time, security and counter-terrorism applications. The tools and methods of Web mining are revealed in an easy-to-understand style, emphasizing the importance of practical, hands-on experience in the creation of successful e-business solutions. The author, a program director for Data and Applications Security at the National Science Foundations, details how both opportunities and dangers on the Web can be identified and managed. Armed with the knowledge contained in this book, businesses can collect and analyze Web-based data to help develop customer relationships, increase sales, and identify existing and potential threats. Organizations can apply these same Web mining techniques to battle the real and present danger of terrorism, demonstrating Web mining's critical role in the intelligence arsenal.

INTRODUCTION Chapter 3 provided a brief introduction to data mining and
discussed its relationship to Web data mining. This chapter provides a brief
introduction to the core technologies for data mining. These core technologies
contribute ...

Advances in Knowledge Discovery and Data Mining

12th Pacific-Asia Conference, PAKDD 2008 Osaka, Japan, May 20-23, 2008 Proceedings

This book constitutes the refereed proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2008, held in Osaka, Japan, in May 2008. The 37 revised long papers, 40 revised full papers, and 36 revised short papers presented together with 1 keynote talk and 4 invited lectures were carefully reviewed and selected from 312 submissions. The papers present new ideas, original research results, and practical development experiences from all KDD-related areas including data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scientific discovery, data visualization, causal induction, and knowledge-based systems.

As data mining having attracted a significant amount of re- search attention, many
clustering methods have been proposed in past decades. However, most of
those techniques have annoying obstacles in precise pattern recognition.

Data Mining for Business Applications

Data Mining for Business Applications presents the state-of-the-art research and development outcomes on methodologies, techniques, approaches and successful applications in the area. The contributions mark a paradigm shift from “data-centered pattern mining” to “domain driven actionable knowledge discovery” for next-generation KDD research and applications. The contents identify how KDD techniques can better contribute to critical domain problems in theory and practice, and strengthen business intelligence in complex enterprise applications. The volume also explores challenges and directions for future research and development in the dialogue between academia and business.

However, in order to efficiently run these data mining algorithms or techniques,
the human actor needs to set up the various parameters. He or she will have to
use their knowledge to make a choice of parameter values that accurately reflect
 ...

Foundations and Novel Approaches in Data Mining

Data-mining has become a popular research topic in recent years for the treatment of the "data rich and information poor" syndrome. Currently, application oriented engineers are only concerned with their immediate problems, which results in an ad hoc method of problem solving. Researchers, on the other hand, lack an understanding of the practical issues of data-mining for real-world problems and often concentrate on issues that are of no significance to the practitioners. In this volume, we hope to remedy problems by (1) presenting a theoretical foundation of data-mining, and (2) providing important new directions for data-mining research. A set of well respected data mining theoreticians were invited to present their views on the fundamental science of data mining. We have also called on researchers with practical data mining experiences to present new important data-mining topics.

Workflow. Mining. Kwang-Hoon Kim1 and Clarence A. Ellis2 1 Collaboration
Technology Research Lab. Department of Computer Science KYONGGI
UNIVERSITY San 94-6 Yiuidong Youngtongku Suwonsi Kyonggido, South Korea
, 442-760 ...

Advances in Data Mining: Applications and Theoretical Aspects

10th Industrial Conference, ICDM 2010, Berlin, Germany, July 12-14, 2010. Proceedings

These are the proceedings of the tenth event of the Industrial Conference on Data Mining ICDM held in Berlin (www.data-mining-forum.de). For this edition the Program Committee received 175 submissions. After the pe- review process, we accepted 49 high-quality papers for oral presentation that are included in this book. The topics range from theoretical aspects of data mining to app- cations of data mining such as on multimedia data, in marketing, finance and telec- munication, in medicine and agriculture, and in process control, industry and society. Extended versions of selected papers will appear in the international journal Trans- tions on Machine Learning and Data Mining (www.ibai-publishing.org/journal/mldm). Ten papers were selected for poster presentations and are published in the ICDM Poster Proceeding Volume by ibai-publishing (www.ibai-publishing.org). In conjunction with ICDM four workshops were held on special hot applicati- oriented topics in data mining: Data Mining in Marketing DMM, Data Mining in LifeScience DMLS, the Workshop on Case-Based Reasoning for Multimedia Data CBR-MD, and the Workshop on Data Mining in Agriculture DMA. The Workshop on Data Mining in Agriculture ran for the first time this year. All workshop papers will be published in the workshop proceedings by ibai-publishing (www.ibai-publishing.org). Selected papers of CBR-MD will be published in a special issue of the international journal Transactions on Case-Based Reasoning (www.ibai-publishing.org/journal/cbr).

10th Industrial Conference, ICDM 2010, Berlin, Germany, July 12-14, 2010.
Proceedings Petra Perner. Re-mining Positive and Negative Association Mining
Results Ayhan Demiriz1, Gurdal Ertek2, Tankut Atan3, and Ufuk Kula1 1 Sakarya
 ...

Data Mining in Public and Private Sectors: Organizational and Government Applications

Organizational and Government Applications

The need for both organizations and government agencies to generate, collect, and utilize data in public and private sector activities is rapidly increasing, placing importance on the growth of data mining applications and tools. Data Mining in Public and Private Sectors: Organizational and Government Applications explores the manifestation of data mining and how it can be enhanced at various levels of management. This innovative publication provides relevant theoretical frameworks and the latest empirical research findings useful to governmental agencies, practicing managers, and academicians.

Data Mining in Public and Private Sectors: Organizational and Government Applications explores the manifestation of data mining and how it can be enhanced at various levels of management.