Michael R. Berthold – författare

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 examplesto 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 open access book constitutes the proceedings of the 18th International Conference on Intelligent Data Analysis, IDA 2020, held in Konstanz, Germany, in April 2020. The 45 full papers presented in this volume were carefully reviewed and selected from 114 submissions.

Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring;

Major updates on techniques and subject coverage (including deep learning) are included. Topics and features: guides the reader through the process of data science, following the interdependent steps of project understanding, data understanding, data blending and transformation, modeling, as well as deployment and monitoring;

The integration of knowledge in the life sciences is continuing apace with ev- increasingimportancebeing placedoncomputer-basedmethodsofdata capture, analysis, and knowledge representation. Today, our many di?erent sciences are providing us with a sea of information: it is the handling of this in?ux that is becoming a key discovery and regulatory question. The solutions to these problems will result in advancements to all of the involved sciences and will be highly in?uential both in the selection of the areas scientists seek to investigate and also on their success. For this to happen, it is crucial to establish an open and lively exchange between computer scientists, biologists, and chemists. To encourage precisely this type of exchange, crossing the borders of the sciences, we organized the 1st Symposium on Computational Life Science in Konstanz, Germany(September 25-27,2005). Themainobjectiveofthesymposiumwasto formbridges,bringingtogetherscientistsfromavarietyofdisciplinestoexchange ideas and research e?orts and to talk about the problems in areas of research that up until now have not been visible at an interdisciplinary level. Our conference program shows that the scienti?c mix worked out very well.From 49 submissions, 21 were selected for presentation at the symposium, c- ering areas ranging from high-level system biology to data analysis related to mass spec traces. As a supplement to the regularconference program,we dedicated one section to papers presentedinthe frameworkof a workshoponDistributed Data Mining in the Life Sciences (LifeDDM), organized by Giuseppe Di Fatta.

This book constitutes the refereed proceedings of the 5th International Conference on Intelligent Data Analysis, IDA 2003, held in Berlin, Germany in August 2003. The 56 revised papers presented were carefully reviewed and selected from 180 submissions. The papers are organized in topical sections on machine learning, probability and topology, classification and pattern recognition, clustering, applications, modeling, and data processing.

This monograph is a detailed introductory presentation of 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, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approaches and Support Vector Machines. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on Visualization and a higher-level overview of the IDA processes, which illustrates the breadth of application of the presented ideas.

This book constitutes the refereed proceedings of the Second International Symposium on Computational Life Sciences, CompLife 2006, held in Cambridge, UK, in September 2006. The 25 revised full papers presented were carefully reviewed and selected from 56 initial submissions. The papers are organized in topical sections on genomics, data mining, molecular simulation, molecular informatics, systems biology, biological networks/metabolism, and computational neuroscience.

This book constitutes the refereed proceedings of the Second International Symposium on Intelligent Data Analysis, IDA-97, held in London, UK, in August 1997.The volume presents 50 revised full papers selected from a total of 107 submissions. Also included is a keynote, Intelligent Data Analysis: Issues and Opportunities, by David J. Hand. The papers are organized in sections on exploratory data analysis, preprocessing and tools; classification and feature selection; medical applications; soft computing; knowledge discovery and data mining; estimation and clustering; data quality; qualitative models.

Formanyyearstheintersectionofcomputing anddataanalysiscontainedme- based statistics packages and not much else. Recently, statisticians have - braced computing, computer scientists have started using statistical theories and methods, and researchers in all corners have invented algorithms to nd structure in vast online datasets. Data analysts now have access to tools for exploratory data analysis, decision tree induction, causal induction, function - timation,constructingcustomizedreferencedistributions,andvisualization,and thereareintelligentassistantsto adviseonmatters ofdesignandanalysis.There aretoolsfortraditional,relativelysmallsamples,andalsoforenormousdatasets. In all, the scope for probing data in new and penetrating ways has never been so exciting. The IDA-99 conference brings together a wide variety of researchers c- cerned with extracting knowledge from data, including people from statistics, machine learning, neural networks, computer science, pattern recognition, da- base management, and other areas.The strategiesadopted by people from these areas are often di erent, and a synergy results if this is recognized.The IDA series of conferences is intended to stimulate interaction between these di erent areas,sothatmorepowerfultoolsemergeforextractingknowledgefromdataand a better understanding is developed of the process of intelligent data analysis. The result is a conference that has a clear focus (one application area:intelligent data analysis) and a broad scope (many di erent methods and techniques).

Weareproudtopresenttheproceedingsoftheseventhbiennialconferenceinthe Intelligent Data Analysis series. The conference took place in Ljubljana, Slo- nia, September 6-8, 2007. IDA continues to expand its scope, quality and size. It started as a small side-symposium as part of a larger conference in 1995 in Baden-Baden(Germany).It quickly attractedmoreinterest in both submissions and attendance as it moved to London (1997) and then Amsterdam (1999). The next three meetings were held in Lisbon (2001), Berlin (2003) and then Madrid in 2005. The improving quality of the submissions has enabled the organizers to assemble programs of ever-increasing consistency and quality. This year we madea rigorousselectionof33papersoutofalmost100submissions.Theresu- ing oral presentations were then scheduled in a single-track, two-and-a-half-day conference program, summarized in the book that you have before you. In accordance with the stated IDA goal of “bringing together researchers from diverse disciplines,” we believe we have achieved an excellent balance of presentationsfromthemoretheoretical–bothstatisticalandmachinelearning– to the more application-oriented areas that illustrate how these techniques can beusedinpractice.Forexample,theproceedingsincludepaperswiththeoretical contributions dealing with statistical approaches to sequence alignment as well as papers addressing practical problems in the areas of text classi?cation and medical data analysis. It is reassuring to see that IDA continues to bring such diverse areas together, thus helping to cross-fertilize these ?elds.

It is our pleasure to present the proceedings of Discovery Science 2008, the 11th International Conference on Discovery Science held in Budapest, Hungary, October 13-16, 2008. It was co-located with ALT 2008, the 19th International Conference on Algorithmic Learning Theory, whose proceedings are available in the twin volume LNAI 5254. This combination of DS and ALT conferences has been successfully organized each year since 2002. It provides a forum for the researchersworking on many di?erent aspects of scienti?c discovery. Indeed, ALT/DS 2008 covered both the possibility to automate part of the scienti?c discoveryandthenecessarysupporttothehumanprocessofdiscoveryinscience. Interestingly, this co-location also provided the opportunity for an exciting joint program of tutorials and invited talks. The number of submitted papers was 58, i.e., slightly more than the previous year. The Program Committee members were involved in a rigorous selection process based on three reviews per paper. At the end, we selected 26 long papers thanks to the recommendations of the experts based on relevance, novelty, signi?cance, technical quality, and clarity. Although some short papers were submitted, none of them was selected.

This monograph is a detailed introductory presentation of 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, ranging from the basic concepts of probability, through general notions of inference, to advanced multivariate and time series methods, as well as a detailed discussion of the increasingly important Bayesian approaches and Support Vector Machines. The following chapters then concentrate on the area of machine learning and artificial intelligence and provide introductions into the topics of rule induction methods, neural networks, fuzzy logic, and stochastic search methods. The book concludes with a chapter on Visualization and a higher-level overview of the IDA processes, which illustrates the breadth of application of the presented ideas.

The background to IDA 2010, the 9th International Symposium on Intelligent DataAnalysis(IDA),is ratherunusual. Previously,thesymposiawereheldbi- nially at European venues. Over this time, the IDA Symposium had established an identity, a dedicated group of Program Committee members, and a regular audience. However, this success had come at a cost to the original ambitions for the symposium - concerned with interfacing AI, statistics and computer science for important and di?cult real-world data analysis problems - being comp- mised in favor of more standard data mining content. IDA 2010 was organized explicitly to re-align the IDA Symposia series with a set of objectives evolved from the original ambitions. This should be construed not as a criticism of r- tine data mining research but rather as an admission that the IDA symposium had taken the path of least resistance with respect to the call for papers and the reviewing process. This is the proceedings volume of IDA 2010, a special event held only a year after the eighth symposium in an attempt to revitalize the area of IDA. There were two major changes compared to previous symposia.First, the Call for - pers (CfP) was completely rewritten, placing great emphasis on algorithms and systems thatsupportmodelling andanalysisofcomplex real-worldsystems. - reover, the CfP explicitly discouraged submissions that might be characterized as "incrementaladvances indata mining algorithms. "Second, the reviewing- chanism was extended to include a "senior ProgrammeCommittee," in response to perceived shortcomings in the existing reviewing process.

Modern knowledge discovery methods enable users to discover complex patterns of various types in large information repositories. However, the underlying assumption has always been that the data to which the methods are applied to originates from one domain. The focus of this book, and the BISON project from which the contributions are originating, is a network based integration of various types of data repositories and the development of new ways to analyse and explore the resulting gigantic information networks. Instead of finding well defined global or local patterns they wanted to find domain bridging associations which are, by definition, not well defined since they will be especially interesting if they are sparse and have not been encountered before. The 32 contributions presented in this state-of-the-art volume together with a detailed introduction to the book are organized in topical sections on bisociation; representation and network creation; network analysis; exploration; and applications and evaluation.