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"This book provides a focal point for research and real-world data mining practitioners that advance knowledge discovery from low-quality data; it presents in-depth experiences and methodologies, providing theoretical and empirical guidance to users who have suffered from underlying low-quality data. Contributions also focus on interdisciplinary collaborations among data quality, data processing, data mining, data privacy, and data sharing"--Provided by publisher.
This comprehensive textbook on data mining details the unique steps of the knowledge discovery process that prescribes the sequence in which data mining projects should be performed, from problem and data understanding through data preprocessing to deployment of the results. This knowledge discovery approach is what distinguishes Data Mining from other texts in this area. The book provides a suite of exercises and includes links to instructional presentations. Furthermore, it contains appendices of relevant mathematical material.
This first textbook on formal concept analysis gives a systematic presentation of the mathematical foundations and their relations to applications in computer science, especially in data analysis and knowledge processing. Above all, it presents graphical methods for representing conceptual systems that have proved themselves in communicating knowledge. The mathematical foundations are treated thoroughly and are illuminated by means of numerous examples, making the basic theory readily accessible in compact form.
This book constitutes the refereed proceedings of the Second International Conference on Formal Concept Analysis, ICFCA 2004, held in Sydney, Australia in February 2004. The 27 revised full papers presented together with 7 invited papers were carefully reviewed and selected for inclusion in the book. Formal concept analysis emerged out of efforts to restructure lattice theory and has been extended into attribute exploration, Boolean judgment, and contextual logics in order to create a powerful general framework for knowledge representation and formal reasoning; among the application areas of formal concept analysis are data and knowledge processing, data visualization, information retrieval, machine learning, data analysis, and knowledge management. The papers in this book address all current issues in formal concept analysis, ranging from foundational and methodological issues to applications in various fields.
This book offers state-of the-art research and development outcomes on methodologies, techniques, approaches and successful applications in domain driven, actionable knowledge discovery. It bridges the gap between business expectations and research output.
Data Mining and Knowledge Discovery Handbook organizes all major concepts, theories, methodologies, trends, challenges and applications of data mining (DM) and knowledge discovery in databases (KDD) into a coherent and unified repository. This book first surveys, then provides comprehensive yet concise algorithmic descriptions of methods, including classic methods plus the extensions and novel methods developed recently. This volume concludes with in-depth descriptions of data mining applications in various interdisciplinary industries including finance, marketing, medicine, biology, engineering, telecommunications, software, and security. Data Mining and Knowledge Discovery Handbook is designed for research scientists and graduate-level students in computer science and engineering. This book is also suitable for professionals in fields such as computing applications, information systems management, and strategic research management.
Knowledge Discovery in the Social Sciences helps readers find valid, meaningful, and useful information. It is written for researchers and data analysts as well as students who have no prior experience in statistics or computer science. Suitable for a variety of classes—including upper-division courses for undergraduates, introductory courses for graduate students, and courses in data management and advanced statistical methods—the book guides readers in the application of data mining techniques and illustrates the significance of newly discovered knowledge. Readers will learn to: • appreciate the role of data mining in scientific research • develop an understanding of fundamental concepts of data mining and knowledge discovery • use software to carry out data mining tasks • select and assess appropriate models to ensure findings are valid and meaningful • develop basic skills in data preparation, data mining, model selection, and validation • apply concepts with end-of-chapter exercises and review summaries
This book contains the proceedings of the second edition of the international Conference on Artificial Intelligence and its Applications (AIAP'21). This edition aims to bring together leading academic scientists, international researchers, and practitioners to exchange and share their experiences and research results on all aspects of Artificial Intelligence. It also provides an interdisciplinary platform for researchers, practitioners and students to present and discuss the most recent innovations, trends, and concerns as well as practical challenges encountered and solutions adopted in the fields of Artificial Intelligence. This international conference offers an opportunity to bridge the gap between the Artificial Intelligence research community and people from the industry or working in other research areas including smart cities, big data, cloud computing, social networks, and energy.
"I think this is an excellent book–I recommend it to anyone involved in molecular epidemiology... The 26 chapters are written by topic specialists, in an explanatory, east to read style." –BTS Newsletter, Summer 2009 "This text provides an accessible and useful handbook for the epidemiologist who wants to survey the field, to become better informed, to look at recent developments and get some background on these or simply to appreciate further the relatively rapid changes in informatic and analytical technologies which increasingly will serve and underpin future epidemiological studies. One of the strengths in this book is the extensive array of practical illustrative examples, and it would also in my opinion have useful potential as a teaching text." –American Journal of Human Biology, March 2009 With the sequencing of the human genome and the mapping of millions of single nucleotide polymorphisms, epidemiology has moved into the molecular domain. Scientists can now use molecular markers to track disease-associated genes in populations, enabling them to study complex chronic diseases that might result from the weak interactions of many genes with the environment. Use of these laboratory generated biomarker data and an understanding of disease mechanisms are increasingly important in elucidating disease aetiology. Molecular Epidemiology of Disease crosses the disciplinary boundaries between laboratory scientists, epidemiologists, clinical researchers and biostatisticians and is accessible to all these relevant research communities in focusing on practical issues of application, rather than reviews of current areas of research. Covers categories of biomarkers of exposure, susceptibility and disease Includes chapters on novel technologies: genomics, transcriptomics, proteomics and metabonomics, which are increasingly finding application in population studies Emphasizes new statistical and bioinformatics approaches necessitated by the large data sets generated using these new methodologies Demonstrates the potential applications of laboratory techniques in tackling epidemiological problems while considering their limitations, including the sources of uncertainty and inaccuracy Discusses issues such as reliability (compared to traditional epidemiological methods) and the timing of exposure Explores practical elements of conducting population studies, including biological repositories and ethics Molecular Epidemiology of Disease provides an easy-to-use, clearly presented handbook that allows epidemiologists to understand the specifics of research involving biomarkers, and laboratory scientists to understand the main issues of epidemiological study design and analysis. It also provides a useful tool for courses on molecular epidemiology, using many examples from population studies to illustrate key concepts and principles.
Effective healthcare delivery is a vital concern for citizens and communities across the globe. The numerous facets of this industry require constant re-evaluation and optimization of management techniques. The Handbook of Research on Healthcare Administration and Management is a pivotal reference source for the latest scholarly material on emerging strategies and methods for delivering optimal healthcare opportunities and solutions. Highlighting issues relating to decision making, process optimization, and technological applications, this book is ideally designed for policy makers, administrators, students, professionals, and researchers interested in achieving superior healthcare solutions.