Classification, Clustering, and Data Analysis: Recent Advances and Applications

Springer Science & Business Media
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The present volume contains a selection of papers presented at the Eighth Conference of the International Federation of Classification Societies (IFCS) which was held in Cracow, Poland, July 16-19, 2002. All originally submitted papers were subject to a reviewing process by two independent referees, a procedure which resulted in the selection of the 53 articles presented in this volume. These articles relate to theoretical investigations as well as to practical applications and cover a wide range of topics in the broad domain of classifi cation, data analysis and related methods. If we try to classify the wealth of problems, methods and approaches into some representative (partially over lapping) groups, we find in particular the following areas: • Clustering • Cluster validation • Discrimination • Multivariate data analysis • Statistical methods • Symbolic data analysis • Consensus trees and phylogeny • Regression trees • Neural networks and genetic algorithms • Applications in economics, medicine, biology, and psychology. Given the international orientation of IFCS conferences and the leading role of IFCS in the scientific world of classification, clustering and data anal ysis, this volume collects a representative selection of current research and modern applications in this field and serves as an up-to-date information source for statisticians, data analysts, data mining specialists and computer scientists.
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Additional Information

Publisher
Springer Science & Business Media
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Published on
Dec 6, 2012
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Pages
508
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ISBN
9783642561818
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Best For
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Language
English
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Genres
Business & Economics / Statistics
Computers / Databases / General
Computers / Information Technology
Computers / Mathematical & Statistical Software
Mathematics / Applied
Mathematics / Discrete Mathematics
Mathematics / Probability & Statistics / General
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Content Protection
This content is DRM protected.
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This valuable resource provides an overview of recent research and strategies in developing and applying modelling to promote practice-based research in STEM education. In doing so, it bridges barriers across academic disciplines by suggesting activities that promote integration of qualitative science concepts with the tools of mathematics and engineering. The volume’s three parts offer a comprehensive review, by

1) Presenting a conceptual background of how scientific inquiry can be induced in mathematics classes considering recommendations of prior research,

2) Collecting case studies that were designed using scientific inquiry process designed for math classes, and

3) Exploring future possibilities and directions for the research included within.

Among the topics discussed:

· STEM education: A platform for multidisciplinary learning.

· Teaching and learning representations in STEM.

· Formulating conceptual framework for multidisciplinary STEM modeling.

· Exploring function continuity in context.

· Exploring function transformations using a dynamic system.

Scientific Inquiry in Mathematics - Theory and Practice delivers hands-on and concrete strategies for effective STEM teaching in practice to educators within the fields of mathematics, science, and technology. It will be of interest to practicing and future mathematics teachers at all levels, as well as teacher educators, mathematics education researchers, and undergraduate and graduate mathematics students interested in research based methods for integrating inquiry-based learning into STEM classrooms.


This volume presents 45 articles dealing with theoretical aspects, methodo logical advances and practical applications in domains relating to classifica tion and clustering, statistical and computational data analysis, conceptual or terminological approaches for information systems, and knowledge struc tures for databases. These articles were selected from about 140 papers presented at the 19th Annual Conference of the Gesellschaft fur Klassifika tion, the German Classification Society. The conference was hosted by W. Polasek at the Institute of Statistics and Econometry of the University of 1 Basel (Switzerland) March 8-10, 1995 . The papers are grouped as follows, where the number in parentheses is the number of papers in the chapter. 1. Classification and clustering (8) 2. Uncertainty and fuzziness (5) 3. Methods of data analysis and applications (7) 4. Statistical models and methods (4) 5. Bayesian learning (5) 6. Conceptual classification, knowledge ordering and information systems (12) 7. Linguistics and dialectometry (4). These chapters are interrelated in many respects. The reader may recogni ze, for example, the analogies and distinctions existing among classification principles developed in such different domains as statistics and information sciences, the benefit to be gained by the comparison of conceptual and ma thematical approaches for structuring data and knowledge, and, finally, the wealth of practical applications described in many of the papers. For convenience of the reader, the content of this volume is briefly reviewed.
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