This volume contains revised versions of selected papers presented dur ing the 23rd Annual Conference of the German Classification Society GfKl (Gesellschaft fiir Klassifikation). The conference took place at the Univer sity of Bielefeld (Germany) in March 1999 under the title "Classification and Information Processing at the Turn of the Millennium". Researchers and practitioners - interested in data analysis, classification, and information processing in the broad sense, including computer science, multimedia, WWW, knowledge discovery, and data mining as well as spe cial application areas such as (in alphabetical order) biology, finance, genome analysis, marketing, medicine, public health, and text analysis - had the op portunity to discuss recent developments and to establish cross-disciplinary cooperation in their fields of interest. Additionally, software and book pre sentations as well as several tutorial courses were organized. The scientific program of the conference included 18 plenary or semi plenary lectures and more than 100 presentations in special sections. The peer-reviewed papers are presented in 5 chapters as follows: • Data Analysis and Classification • Computer Science, Computational Statistics, and Data Mining • Management Science, Marketing, and Finance • Biology, Genome Analysis, and Medicine • Text Analysis and Information Retrieval As an unambiguous assignment of results to single chapters is sometimes difficult papers are grouped in a way that the editors found appropriate.

Quantification of categorical, or non-numerical, data is a problem that scientists face across a wide range of disciplines. Exploring data analysis in various areas of research, such as the social sciences and biology, Multidimensional Nonlinear Descriptive Analysis presents methods for analyzing categorical data that are not necessarily sampled randomly from a normal population and often involve nonlinear relations. This reference not only provides an overview of multidimensional nonlinear descriptive analysis (MUNDA) of discrete data, it also offers new results in a variety of fields. The first part of the book covers conceptual and technical preliminaries needed to understand the data analysis in subsequent chapters. The next two parts contain applications of MUNDA to diverse data types, with each chapter devoted to one type of categorical data, a brief historical comment, and basic skills peculiar to the data types. The final part examines several problems and then concludes with suggestions for future progress. Covering both the early and later years of MUNDA research in the social sciences, psychology, ecology, biology, and statistics, this book provides a framework for potential developments in even more areas of study.

Library Classification Trends in the 21st Century traces development in and around library classification as reported in literature published in the first decade of the 21st century. It reviews literature published on various aspects of library classification, including modern applications of classification such as internet resource discovery, automatic book classification, text categorization, modern manifestations of classification such as taxonomies, folksonomies and ontologies and interoperable systems enabling crosswalk. The book also features classification education and an exploration of relevant topics. Covers all aspects of library classification It is the only book that reviews literature published over a decade’s time span (1999-2009) Well thought chapterization which is in tune with the LIS and classification curriculum

From a linguistic perspective, it is quanti?cation which makes all the di?- ence between “having no dollars” and “having a lot of dollars”. And it is the meaning of the quanti?er “most” which eventually decides if “Most Ame- cans voted Kerry” or “Most Americans voted Bush” (as it stands). Natural language(NL)quanti?erslike“all”,“almostall”,“many”etc. serveanimp- tant purpose because they permit us to speak about properties of collections, as opposed to describing speci?c individuals only; in technical terms, qu- ti?ers are a ‘second-order’ construct. Thus the quantifying statement “Most Americans voted Bush” asserts that the set of voters of George W. Bush c- prisesthemajorityofAmericans,while“Bushsneezes”onlytellsussomething about a speci?c individual. By describing collections rather than individuals, quanti?ers extend the expressive power of natural languages far beyond that of propositional logic and make them a universal communication medium. Hence language heavily depends on quantifying constructions. These often involve fuzzy concepts like “tall”, and they frequently refer to fuzzy quantities in agreement like “about ten”, “almost all”, “many” etc. In order to exploit this expressive power and make fuzzy quanti?cation available to technical applications, a number of proposals have been made how to model fuzzy quanti?ers in the framework of fuzzy set theory. These approaches usually reduce fuzzy quanti?cation to a comparison of scalar or fuzzy cardinalities [197, 132].

The contributions in this volume represent the latest research results in the field of Classification, Clustering, and Data Analysis. Besides the theoretical analysis, papers focus on various application fields as Archaeology, Astronomy, Bio-Sciences, Business, Electronic Data and Web, Finance and Insurance, Library Science and Linguistics, Marketing, Music Science, and Quality Assurance.

These three volumes (CCIS 442, 443, 444) constitute the proceedings of the 15th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2014, held in Montpellier, France, July 15-19, 2014. The 180 revised full papers presented together with five invited talks were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on uncertainty and imprecision on the web of data; decision support and uncertainty management in agri-environment; fuzzy implications; clustering; fuzzy measures and integrals; non-classical logics; data analysis; real-world applications; aggregation; probabilistic networks; recommendation systems and social networks; fuzzy systems; fuzzy logic in boolean framework; management of uncertainty in social networks; from different to same, from imitation to analogy; soft computing and sensory analysis; database systems; fuzzy set theory; measurement and sensory information; aggregation; formal methods for vagueness and uncertainty in a many-valued realm; graduality; preferences; uncertainty management in machine learning; philosophy and history of soft computing; soft computing and sensory analysis; similarity analysis; fuzzy logic, formal concept analysis and rough set; intelligent databases and information systems; theory of evidence; aggregation functions; big data - the role of fuzzy methods; imprecise probabilities: from foundations to applications; multinomial logistic regression on Markov chains for crop rotation modelling; intelligent measurement and control for nonlinear systems.

This three volume set (CCIS 1237-1239) constitutes the proceedings of the 18th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2020, in June 2020. The conference was scheduled to take place in Lisbon, Portugal, at University of Lisbon, but due to COVID-19 pandemic it was held virtually. The 173 papers were carefully reviewed and selected from 213 submissions. The papers are organized in topical sections: homage to Enrique Ruspini; invited talks; foundations and mathematics; decision making, preferences and votes; optimization and uncertainty; games; real world applications; knowledge processing and creation; machine learning I; machine learning II; XAI; image processing; temporal data processing; text analysis and processing; fuzzy interval analysis; theoretical and applied aspects of imprecise probabilities; similarities in artificial intelligence; belief function theory and its applications; aggregation: theory and practice; aggregation: pre-aggregation functions and other generalizations of monotonicity; aggregation: aggregation of different data structures; fuzzy methods in data mining and knowledge discovery; computational intelligence for logistics and transportation problems; fuzzy implication functions; soft methods in statistics and data analysis; image understanding and explainable AI; fuzzy and generalized quantifier theory; mathematical methods towards dealing with uncertainty in applied sciences; statistical image processing and analysis, with applications in neuroimaging; interval uncertainty; discrete models and computational intelligence; current techniques to model, process and describe time series; mathematical fuzzy logic and graded reasoning models; formal concept analysis, rough sets, general operators and related topics; computational intelligence methods in information modelling, representation and processing.

This volume collects revised versions of papers presented at the 29th Annual Conference of the Gesellschaft für Klassifikation, the German Classification Society, held at the Otto-von-Guericke-University of Magdeburg, Germany, in March 2005. In addition to traditional subjects like Classification, Clustering, and Data Analysis, converage extends to a wide range of topics relating to Computer Science: Text Mining, Web Mining, Fuzzy Data Analysis, IT Security, Adaptivity and Personalization, and Visualization.

This volume focuses on innovative approaches and recent developments in clustering, analysis of data and models, and applications: The first part of the book covers a broad range of innovations in the area of clustering, from algorithmic innovations for graph clustering to new visualization and evaluation techniques. The second part addresses new developments in data and decision analysis (conjoint analysis, non-additive utility functions, analysis of asymmetric relationships, and regularization techniques). The third part is devoted to the application of innovative data analysis methods in the life-sciences, the social sciences and in engineering. All contributions in this volume are revised and extended versions of selected papers presented in the German/Japanese Workshops at Karlsruhe (2010) and Kyoto (2012).

Big data and machine learning are driving the Fourth Industrial Revolution. With the age of big data upon us, we risk drowning in a flood of digital data. Big data has now become a critical part of both the business world and daily life, as the synthesis and synergy of machine learning and big data has enormous potential. Big data and machine learning are projected to not only maximize citizen wealth, but also promote societal health. As big data continues to evolve and the demand for professionals in the field increases, access to the most current information about the concepts, issues, trends, and technologies in this interdisciplinary area is needed. The Encyclopedia of Data Science and Machine Learning examines current, state-of-the-art research in the areas of data science, machine learning, data mining, and more. It provides an international forum for experts within these fields to advance the knowledge and practice in all facets of big data and machine learning, emphasizing emerging theories, principals, models, processes, and applications to inspire and circulate innovative findings into research, business, and communities. Covering topics such as benefit management, recommendation system analysis, and global software development, this expansive reference provides a dynamic resource for data scientists, data analysts, computer scientists, technical managers, corporate executives, students and educators of higher education, government officials, researchers, and academicians.