This book constitutes the refereed proceedings of the 7th International Conference on Intelligent Data Analysis, IDA 2007, held in Ljubljana, Slovenia. The 33 revised papers were carefully reviewed and selected from almost 100 submissions. The book covers all current aspects of this interdisciplinary field, including statistics, machine learning, data mining, classification and pattern recognition, clustering, applications, modeling, and interactive dynamic data visualization.
This second and revised edition contains a detailed introduction to the key classes of intelligent data analysis methods. The twelve coherently written chapters by leading experts provide complete coverage of the core issues. The first half of the book is devoted to the discussion of classical statistical issues. The following chapters concentrate on machine learning and artificial intelligence, rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on visualization and an advanced overview of IDA processes.
This book constitutes the proceedings of the XVIII International Conference on Data Science and Intelligent Analysis of Information (ICDSIAI'2018), held in Kiev, Ukraine on June 4-7, 2018. The conference series, which dates back to 2001 when it was known as the Workshop on Intelligent Analysis of Information, was renamed in 2008 to reflect the broadening of its scope and the composition of its organizers and participants. ICDSIAI'2018 brought together a large number of participants from numerous countries in Europe, Asia and the USA. The papers presented addressed novel theoretical developments in methods, algorithms and implementations for the broadly perceived areas of big data mining and intelligent analysis of data and information, representation and processing of uncertainty and fuzziness, including contributions on a range of applications in the fields of decision-making and decision support, economics, education, ecology, law, and various areas of technology. The book is dedicated to the memory of the conference founder, the late Professor Tetiana Taran, an outstanding scientist in the field of artificial intelligence whose research record, vision and personality have greatly contributed to the development of Ukrainian artificial intelligence and computer science.
This book gathers papers presented at the ECC 2016, the Third Euro-China Conference on Intelligent Data Analysis and Applications, which was held in Fuzhou City, China from November 7 to 9, 2016. The aim of the ECC is to provide an internationally respected forum for scientific research in the broad areas of intelligent data analysis, computational intelligence, signal processing, and all associated applications of artificial intelligence (AI). The third installment of the ECC was jointly organized by Fujian University of Technology, China, and VSB-Technical University of Ostrava, Czech Republic. The conference was co-sponsored by Taiwan Association for Web Intelligence Consortium, and Immersion Co., Ltd.
One of the superb characteristics of Intelligent Data Analysis (IDA) is that it is an interdisciplinary ?eld in which researchers and practitioners from a number of areas are involved in a typical project. This also creates a challenge in which the success of a team depends on the participation of users and domain experts who need to interact with researchers and developers of any IDA system. All this is usually re?ected in successful projects and of course on the papers that were evaluated by this year’s program committee from which the ?nal program has been developed. In our call for papers, we solicited papers on (i) applications and tools, (ii) theory and general principles, and (iii) algorithms and techniques. We received a total of 184 papers, reviewing these was a major challenge. Each paper was assigned to three reviewers. In the end 46 papers were accepted, which are all included in the proceedings and presented at the conference. This year’s papers re?ect the results of applied and theoretical researchfrom a number of disciplines all of which are related to the ?eld of Intelligent Data Analysis. To have the best combination of theoretical and applied research and also provide the best focus, we have divided this year’s IDA program into tu- rials, invited talks, panel discussions and technical sessions.
This book constitutes the refereed proceedings of the Third International Symposium on Intelligent Data Analysis, IDA-99 held in Amsterdam, The Netherlands in August 1999. The 21 revised full papers and 23 posters presented in the book were carefully reviewed and selected from a total of more than 100 submissions. The papers address all current aspects of intelligent data analysis; they are organized in sections on learning, visualization, classification and clustering, integration, applications and media mining.
This book constitutes the proceedings of the 20th International Symposium on Intelligent Data Analysis, IDA 2022, which was held in Rennes, France, during April 20-22, 2022. The 31 papers included in this book were carefully reviewed and selected from 73 submissions. They deal with high quality, novel research in intelligent data analysis.
Intelligent data analysis, data mining and knowledge discovery in databases have recently gained the attention of a large number of researchers and practitioners. This is witnessed by the rapidly increasing number of submissions and participants at related conferences and workshops, by the emergence of new journals in this area (e.g., Data Mining and Knowledge Discovery, Intelligent Data Analysis, etc.), and by the increasing number of new applications in this field. In our view, the awareness of these challenging research fields and emerging technologies has been much larger in industry than in medicine and pharmacology. The main purpose of this book is to present the various techniques and methods that are available for intelligent data analysis in medicine and pharmacology, and to present case studies of their application. Intelligent Data Analysis in Medicine and Pharmacology consists of selected (and thoroughly revised) papers presented at the First International Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-96) held in Budapest in August 1996 as part of the 12th European Conference on Artificial Intelligence (ECAI-96), IDAMAP-96 was organized with the motivation to gather scientists and practitioners interested in computational data analysis methods applied to medicine and pharmacology, aimed at narrowing the increasing gap between excessive amounts of data stored in medical and pharmacological databases on the one hand, and the interpretation, understanding and effective use of stored data on the other hand. Besides the revised Workshop papers, the book contains a selection of contributions by invited authors. The expected readership of the book is researchers and practitioners interested in intelligent data analysis, data mining, and knowledge discovery in databases, particularly those who are interested in using these technologies in medicine and pharmacology. Researchers and students in artificial intelligence and statistics should find this book of interest as well. Finally, much of the presented material will be interesting to physicians and pharmacologists challenged by new computational technologies, or simply in need of effectively utilizing the overwhelming volumes of data collected as a result of improved computer support in their daily professional practice.
Each passing year bears witness to the development of ever more powerful computers, increasingly fast and cheap storage media, and even higher bandwidth data connections. This makes it easy to believe that we can now – at least in principle – solve any problem we are faced with so long as we only have enough data. Yet this is not the case. Although large databases allow us to retrieve many different single pieces of information and to compute simple aggregations, general patterns and regularities often go undetected. Furthermore, it is exactly these patterns, regularities and trends that are often most valuable. To avoid the danger of “drowning in information, but starving for knowledge” the branch of research known as data analysis has emerged, and a considerable number of methods and software tools have been developed. However, it is not these tools alone but the intelligent application of human intuition in combination with computational power, of sound background knowledge with computer-aided modeling, and of critical reflection with convenient automatic model construction, that results in successful intelligent data analysis projects. Guide to Intelligent Data Analysis provides a hands-on instructional approach to many basic data analysis techniques, and explains how these are used to solve data analysis problems. Topics and features: guides the reader through the process of data analysis, following the interdependent steps of project understanding, data understanding, data preparation, modeling, and deployment and monitoring; equips the reader with the necessary information in order to obtain hands-on experience of the topics under discussion; provides a review of the basics of classical statistics that support and justify many data analysis methods, and a glossary of statistical terms; includes numerous examples using R and KNIME, together with appendices introducing the open source software; integrates illustrations and case-study-style examples to support pedagogical exposition. This practical and systematic textbook/reference for graduate and advanced undergraduate students is also essential reading for all professionals who face data analysis problems. Moreover, it is a book to be used following one’s exploration of it. Dr. Michael R. Berthold is Nycomed-Professor of Bioinformatics and Information Mining at the University of Konstanz, Germany. Dr. Christian Borgelt is Principal Researcher at the Intelligent Data Analysis and Graphical Models Research Unit of the European Centre for Soft Computing, Spain. Dr. Frank Höppner is Professor of Information Systems at Ostfalia University of Applied Sciences, Germany. Dr. Frank Klawonn is a Professor in the Department of Computer Science and Head of the Data Analysis and Pattern Recognition Laboratory at Ostfalia University of Applied Sciences, Germany. He is also Head of the Bioinformatics and Statistics group at the Helmholtz Centre for Infection Research, Braunschweig, Germany.
This book constitutes the proceedings of the 21st International Symposium on Intelligent Data Analysis, IDA 2022, which was held in Louvain-la-Neuve, Belgium, during April 12-14, 2023. The 38 papers included in this book were carefully reviewed and selected from 91 submissions. IDA is an international symposium presenting advances in the intelligent analysis of data. Distinguishing characteristics of IDA are its focus on novel, inspiring ideas, its focus on research, and its relatively small scale.
