Inductive Databases and Constraint-Based Data Mining
Inductive Databases and Constraint-Based Data Mining

Author: Sašo Džeroski

Publisher: Springer Science & Business Media

Published: 2010-11-18

Total Pages: 458

ISBN-13: 1441977384

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This book is about inductive databases and constraint-based data mining, emerging research topics lying at the intersection of data mining and database research. The aim of the book as to provide an overview of the state-of- the art in this novel and - citing research area. Of special interest are the recent methods for constraint-based mining of global models for prediction and clustering, the uni?cation of pattern mining approaches through constraint programming, the clari?cation of the re- tionship between mining local patterns and global models, and the proposed in- grative frameworks and approaches for inducive databases. On the application side, applications to practically relevant problems from bioinformatics are presented. Inductive databases (IDBs) represent a database view on data mining and kno- edge discovery. IDBs contain not only data, but also generalizations (patterns and models) valid in the data. In an IDB, ordinary queries can be used to access and - nipulate data, while inductive queries can be used to generate (mine), manipulate, and apply patterns and models. In the IDB framework, patterns and models become ”?rst-class citizens” and KDD becomes an extended querying process in which both the data and the patterns/models that hold in the data are queried.

Predictive Clustering
Predictive Clustering

Author: Hendrik Blockeel

Publisher: Springer

Published: 2012-05-31

Total Pages: 240

ISBN-13: 9781461411468

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This book introduces a novel paradigm for machine learning and data mining called predictive clustering, which covers a broad variety of learning tasks and offers a fresh perspective on existing techniques. The book presents an informal introduction to predictive clustering, describing learning tasks and settings, and then continues with a formal description of the paradigm, explaining algorithms for learning predictive clustering trees and predictive clustering rules, as well as presenting the applicability of these learning techniques to a broad range of tasks. Variants of decision tree learning algorithms are also introduced. Finally, the book offers several significant applications in ecology and bio-informatics. The book is written in a straightforward and easy-to-understand manner, aimed at varied readership, ranging from researchers with an interest in machine learning techniques to practitioners of data mining technology in the areas of ecology and bioinformatics.

Knowledge Discovery in Inductive Databases
Knowledge Discovery in Inductive Databases

Author: Francesco Bonchi

Publisher: Springer

Published: 2006-03-05

Total Pages: 259

ISBN-13: 3540332936

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This book presents the thoroughly refereed joint postproceedings of the 4th International Workshop on Knowledge Discovery in Inductive Databases, October 2005. 20 revised full papers presented together with 2 are reproduced here. Bringing together the fields of databases, machine learning, and data mining, the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.

Clustering
Clustering

Author: Rui Xu

Publisher: John Wiley & Sons

Published: 2008-11-03

Total Pages: 400

ISBN-13: 0470382783

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This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.

Advances in Knowledge Discovery and Data Mining
Advances in Knowledge Discovery and Data Mining

Author: Takashi Washio

Publisher: Springer

Published: 2008-05-11

Total Pages: 1126

ISBN-13: 3540681256

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ThePaci?c-AsiaConferenceonKnowledgeDiscoveryandDataMining(PAKDD) has been held every year since 1997. PAKDD 2008, the 12th in the series, was heldatOsaka,JapanduringMay20–23,2008.PAKDDisaleadinginternational conference in the area of data mining. It provides an international forum for - searchers and industry practitioners to share their new ideas, original research results, and practical development experiences from all KDD-related areas - cluding data mining, data warehousing, machine learning, databases, statistics, knowledge acquisition, automatic scienti?c discovery, data visualization, causal induction, and knowledge-based systems. This year we received a total of 312 research papers from 34 countries and regions in Asia, Australia, North America, South America, Europe, and Africa. Every submitted paper was rigorously reviewed by two or three reviewers, d- cussed by the reviewers under the supervision of an Area Chair, and judged by the Program Committee Chairs. When there was a disagreement, the Area Chair and/or the Program Committee Chairs provided an additional review. Thus, many submissions were reviewed by four experts. The Program Comm- tee members were deeply involved in a highly selective process. As a result, only approximately11.9%ofthe312submissionswereacceptedaslongpapers,12.8% of them were accepted as regular papers, and 11.5% of them were accepted as short papers.

Discovery Science
Discovery Science

Author: Johannes Fürnkranz

Publisher: Springer

Published: 2013-09-30

Total Pages: 371

ISBN-13: 3642408974

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This book constitutes the proceedings of the 16th International Conference on Discovery Science, DS 2013, held in Singapore in October 2013, and co-located with the International Conference on Algorithmic Learning Theory, ALT 2013. The 23 papers presented in this volume were carefully reviewed and selected from 52 submissions. They cover recent advances in the development and analysis of methods of automatic scientific knowledge discovery, machine learning, intelligent data analysis, and their application to knowledge discovery.

Knowledge Discovery in Inductive Databases
Knowledge Discovery in Inductive Databases

Author: Saso Dzeroski

Publisher: Springer

Published: 2007-09-29

Total Pages: 310

ISBN-13: 3540755497

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This book constitutes the thoroughly refereed joint postproceedings of the 5th International Workshop on Knowledge Discovery in Inductive Databases, KDID 2006, held in association with ECML/PKDD. Bringing together the fields of databases, machine learning, and data mining, the papers address various current topics in knowledge discovery and data mining in the framework of inductive databases such as constraint-based mining, database technology and inductive querying.

Advances in Bioinformatics and Computational Biology
Advances in Bioinformatics and Computational Biology

Author: Marcilio C.P. de Souto

Publisher: Springer

Published: 2012-08-10

Total Pages: 214

ISBN-13: 3642319270

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This book constitutes the refereed proceedings of the 7th Brazilian Symposium on Bioinformatics, BSB 2012, held in Campo Grande, Brazil, in August 2012. The 16 regular papers presented were carefully reviewed and selected for inclusion in this book. It also contains a joint paper from two of the guest speakers. The Brazilian Symposium on Bioinformatics covers all aspects of bioinformatics and computational biology, including sequence analysis; motifs, and pattern matching; biological databases, data management, data integration, and data mining; biomedical text mining; structural, comparative, and functional genomics; personal genomics; protein structure, modeling, and simulation; gene identification, regulation and expression analysis; gene and protein interaction and networks; molecular docking; molecular evolution and phylogenetics; computational systems biology; computational proteomics; statistical analysis of molecular sequences; algorithms for problems in computational biology; applications in molecular biology, biochemistry, genetics, medicine, microbiology and associated subjects.

Disease Prediction using Machine Learning, Deep Learning and Data Analytics
Disease Prediction using Machine Learning, Deep Learning and Data Analytics

Author: Geeta Rani, Vijaypal Singh Dhaka, Pradeep Kumar Tiwari

Publisher: Bentham Science Publishers

Published: 2024-03-07

Total Pages: 196

ISBN-13: 9815179136

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This book is a comprehensive review of technologies and data in healthcare services. It features a compilation of 10 chapters that inform readers about the recent research and developments in this field. Each chapter focuses on a specific aspect of healthcare services, highlighting the potential impact of technology on enhancing practices and outcomes. The main features of the book include 1) referenced contributions from healthcare and data analytics experts, 2) a broad range of topics that cover healthcare services, and 3) demonstration of deep learning techniques for specific diseases. Key topics: - Federated learning in analysis of sensitive healthcare data while preserving privacy and security. - Artificial intelligence for 3-D bone image reconstruction. - Detection of disease severity and creating personalized treatment plans using machine learning and software tools - Case studies for disease detection methods for different disease and conditions, including dementia, asthma, eye diseases - Brain-computer interfaces - Data mining for standardized electronic health records - Data collection, management, and analysis in epidemiological research The book is a resource for learners and professionals in healthcare service training programs and health administration departments. Readership Learners and professionals in healthcare service training programs and health administration departments.

Machine Learning
Machine Learning

Author: Peter Flach

Publisher: Cambridge University Press

Published: 2012-09-20

Total Pages: 415

ISBN-13: 1107096391

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Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.