This book constitutes the proceedings of the 12th International Conference on Advanced Data Mining and Applications, ADMA 2016, held in Gold Coast, Australia, in December 2016. The 70 papers presented in this volume were carefully reviewed and selected from 105 submissions. The selected papers covered a wide variety of important topics in the area of data mining, including parallel and distributed data mining algorithms, mining on data streams, graph mining, spatial data mining, multimedia data mining, Web mining, the Internet of Things, health informatics, and biomedical data mining.
Author: Ronghuai Huang
Publisher: Springer Science & Business Media
Release Date: 2009-07-28
This book constitutes the refereed proceedings of the 5th International Conference on Advanced Data Mining and Applications, ADMA 2009, held in Beijing, China, in August 2009. The 34 revised full papers and 47 revised short papers presented together with the abstract of 4 keynote lectures were carefully reviewed and selected from 322 submissions from 27 countries. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining and feature original research results in data mining, spanning applications, algorithms, software and systems, and different applied disciplines with potential in data mining.
Author: Usama M. Fayyad
Publisher: Mit Press
Release Date: 1996
Eight sections of this book span fundamental issues of knowledge discovery, classification and clustering, trend and deviation analysis, dependency derivation, integrated discovery systems, augumented database systems and application case studies. The appendices provide a list of terms used in the literature of the field of data mining and knowledge discovery in databases, and a list of online resources for the KDD researcher.
Author: Ian H. Witten
Publisher: Morgan Kaufmann
Release Date: 1999
Genre: Business & Economics
In this fully updated second edition of the highly acclaimed Managing Gigabytes, authors Witten, Moffat, and Bell continue to provide unparalleled coverage of state-of-the-art techniques for compressing and indexing data. Whatever your field, if you work with large quantities of information, this book is essential reading--an authoritative theoretical resource and a practical guide to meeting the toughest storage and access challenges. It covers the latest developments in compression and indexing and their application on the Web and in digital libraries. It also details dozens of powerful techniques supported by mg, the authors' own system for compressing, storing, and retrieving text, images, and textual images. mg's source code is freely available on the Web. * Up-to-date coverage of new text compression algorithms such as block sorting, approximate arithmetic coding, and fat Huffman coding * New sections on content-based index compression and distributed querying, with 2 new data structures for fast indexing * New coverage of image coding, including descriptions of de facto standards in use on the Web (GIF and PNG), information on CALIC, the new proposed JPEG Lossless standard, and JBIG2 * New information on the Internet and WWW, digital libraries, web search engines, and agent-based retrieval * Accompanied by a public domain system called MG which is a fully worked-out operational example of the advanced techniques developed and explained in the book * New appendix on an existing digital library system that uses the MG software
This book constitutes the refereed proceedings of the 14th International Conference on Practical Applications of Scalable Multi-agent Systems, PAAMS 2016, held in Sevilla, Spain, in June 2016. The 9 revised full papers, 10 short papers, and 16 Demo papers were carefully reviewed and selected from 58 submissions (39 full paper and 19 Demo paper submissions. The papers report on the application and validation of agent-based models, methods, and technologies in a number of key application areas, including day life and real world, energy and networks, human and trust, markets and bids, models and tools, negotiation and conversation, scalability and resources.
Author: Bruce Croft
Publisher: Pearson Higher Ed
Release Date: 2011-11-21
This is the eBook of the printed book and may not include any media, website access codes, or print supplements that may come packaged with the bound book. Search Engines: Information Retrieval in Practice is ideal for introductory information retrieval courses at the undergraduate and graduate level in computer science, information science and computer engineering departments. It is also a valuable tool for search engine and information retrieval professionals. Written by a leader in the field of information retrieval, Search Engines: Information Retrieval in Practice , is designed to give undergraduate students the understanding and tools they need to evaluate, compare and modify search engines. Coverage of the underlying IR and mathematical models reinforce key concepts. The book’s numerous programming exercises make extensive use of Galago, a Java-based open source search engine.
This book constitutes the refereed proceedings of the 27th International Conference on Algorithmic Learning Theory, ALT 2016, held in Bari, Italy, in October 2016, co-located with the 19th International Conference on Discovery Science, DS 2016. The 24 regular papers presented in this volume were carefully reviewed and selected from 45 submissions. In addition the book contains 5 abstracts of invited talks. The papers are organized in topical sections named: error bounds, sample compression schemes; statistical learning, theory, evolvability; exact and interactive learning; complexity of teaching models; inductive inference; online learning; bandits and reinforcement learning; and clustering.
This book presents the current trends, technologies, and challenges in Big Data in the diversified field of engineering and sciences. It covers the applications of Big Data ranging from conventional fields of mechanical engineering, civil engineering to electronics, electrical, and computer science to areas in pharmaceutical and biological sciences. This book consists of contributions from various authors from all sectors of academia and industries, demonstrating the imperative application of Big Data for the decision-making process in sectors where the volume, variety, and velocity of information keep increasing. The book is a useful reference for graduate students, researchers and scientists interested in exploring the potential of Big Data in the application of engineering areas.
This book constitutes the refereed proceedings of the 15th International Conference of the Italian Association for Artificial Intelligence, AI*IA 2016, held in Genova, Italy, in November/December 2016. The 39 full papers presented were carefully reviewed and selected from 53 submissions. The papers are organized in topical sections on optimization and evolutionary algorithms; classification, pattern recognition, and computer vision; multi-agent systems; machine learning; semantic web and description logics; natural language processing; planning and scheduling; and formal verification.
Author: Limsoon Wong
Publisher: World Scientific
Release Date: 2004-05-24
Computer scientists have increasingly been enlisted as “bioinformaticians” to assist molecular biologists in their research. This book is a practical introduction to bioinformatics for these computer scientists. The chapters are in-depth discussions by expert bioinformaticians on both general techniques and specific approaches to a range of selected bioinformatics problems. The book is organized into clusters of chapters on the following topics: • Overview of modern molecular biology and a broad spectrum of techniques from computer science — data mining, machine learning, mathematical modeling, sequence alignment, data integration, workflow development, etc. • In-depth discussion of computational recognition of functional and regulatory sites in DNA sequences. • Incisive discussion of computational prediction of secondary structure of RNA sequences. • Overview of computational prediction of protein cellular localization, and selected discussions of inference of protein function. • Overview of methods for discovering protein–protein interactions. • Detailed discussion of approaches to gene expression analysis for the diagnosis of diseases, the treatment of diseases, and the understanding of gene functions. • Case studies on analysis of phylogenies, functional annotation of proteins, construction of purpose-built integrated biological databases, and development of workflows underlying the large-scale-effort gene discovery. Contents:Molecular Biology for the Practical BioinformaticianStrategy and Planning of Bioinformatics ExperimentsData Mining Techniques for the Practical BioinformaticianTechniques for Recognition of Translation Initiation SitesHow Neural Networks Find Promoters Using Recognition of Micro-Structural Promoter ComponentsNeural-Statistical Model of TATA-Box Motifs in EukaryotesTuning the Dragon Promoter Finder System for Human Promoter RecognitionRNA Secondary Structure PredictionProtein Localization PredictionHomology Search MethodsAnalysis of Phylogeny: A Case Study on SaururaceaeFunctional Annotation and Protein Families: From Theory to PracticeDiscovering Protein–Protein InteractionsTechniques for Analysis of Gene ExpressionGenome-Wide cDNA Oligo Probe Design and Its Applications in i>Schizosaccharomyces PombeMining New Motifs from cDNA Sequence DataTechnologies for Biological Data IntegrationConstruction of Biological Databases: A Case Study on the Protein Phosphatase DataBase (PPDB)A Family Classification Approach to Functional Annotation of ProteinsInformatics for Efficient EST-Based Gene Discovery in Normalized and Subtracted cDNA Libraries Readership: Computer scientists planning to be a bioinformatician; computer science undergraduates in their sophomore and/or senior years. Keywords:Regularotory Site Recognition;Sequence Homology Search;Gene Expression Analysis;Protein Functional Annotation;RNA Secondary Structure Prediction;Computational Gene Discovery;Bioinformatics;Data MiningKey Features:Written in a practical, in-depth tutorial styleCovers a broad range of bioinformatics topics and of techniques used in bioinformaticsComprehensive overviews of the development of various approaches in a number of selected topicsIn-depth exposition of a number of important topicsContributions by prominent researchers: Vladimir Bajic, Ming Li, Kenta Nakai, Limsoon Wong, Cathy Wu, etc.Extensive, integrated references to background literature
Author: Colin H. Hansen
Publisher: John Wiley & Sons
Release Date: 2017-01-31
Genre: Technology & Engineering
A comprehensive guide to wind farm noise prediction, measurement, assessment, control and effects on people Wind Farm Noise covers all aspects associated with the generation, measurement, propagation, regulation and adverse health effects of noise produced by large horizontal-axis wind turbines of the type used in wind farms. The book begins with a brief history of wind turbine development and the regulation of their noise at sensitive receivers. Also included is an introductory chapter on the fundamentals of acoustics relevant to wind turbine noise so that readers are well prepared for understanding later chapters on noise measurements, noise generation mechanisms, noise propagation modelling and the assessment of the noise at surrounding residences. Key features: Potential adverse health effects of wind farm noise are discussed in an objective way. Means for calculating the noise at residences due to a wind farm prior to construction are covered in detail along with uncertainty estimates. The effects of meteorological conditions and other influences, such as obstacles, ground cover and atmospheric absorption, on noise levels at residences are explained. Quantities that should be measured as well as how to best measure them in order to properly characterise wind farm noise are discussed in detail. Noise generation mechanisms and possible means for their control are discussed as well as aspects of wind farm noise that still require further research to be properly understood. The book provides comprehensive coverage of the topic, containing both introductory and advanced level material.
This book constitutes the refereed proceedings of the 29th Canadian Conference on Artificial Intelligence, Canadian AI 2016, held in Victoria, BC, Canada, in May/June 2016. The 12 full papers and 27 short papers presented were carefully reviewed and selected from 97 submissions. The focus of the conference was on the following subjects: actions and behaviours, audio and visual recognition, natural language processing, reasoning and learning, streams and distributed computing.
This two-volume set, LNAI 9651 and 9652, constitutes the thoroughly refereed proceedings of the 20th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2016, held in Auckland, New Zealand, in April 2016. The 91 full papers were carefully reviewed and selected from 307 submissions. They are organized in topical sections named: classification; machine learning; applications; novel methods and algorithms; opinion mining and sentiment analysis; clustering; feature extraction and pattern mining; graph and network data; spatiotemporal and image data; anomaly detection and clustering; novel models and algorithms; and text mining and recommender systems.
Author: Geoffrey McLachlan
Publisher: John Wiley & Sons
Release Date: 2007-11-09
The only single-source——now completely updated and revised——to offer a unified treatment of the theory, methodology, and applications of the EM algorithm Complete with updates that capture developments from the past decade, The EM Algorithm and Extensions, Second Edition successfully provides a basic understanding of the EM algorithm by describing its inception, implementation, and applicability in numerous statistical contexts. In conjunction with the fundamentals of the topic, the authors discuss convergence issues and computation of standard errors, and, in addition, unveil many parallels and connections between the EM algorithm and Markov chain Monte Carlo algorithms. Thorough discussions on the complexities and drawbacks that arise from the basic EM algorithm, such as slow convergence and lack of an in-built procedure to compute the covariance matrix of parameter estimates, are also presented. While the general philosophy of the First Edition has been maintained, this timely new edition has been updated, revised, and expanded to include: New chapters on Monte Carlo versions of the EM algorithm and generalizations of the EM algorithm New results on convergence, including convergence of the EM algorithm in constrained parameter spaces Expanded discussion of standard error computation methods, such as methods for categorical data and methods based on numerical differentiation Coverage of the interval EM, which locates all stationary points in a designated region of the parameter space Exploration of the EM algorithm's relationship with the Gibbs sampler and other Markov chain Monte Carlo methods Plentiful pedagogical elements—chapter introductions, lists of examples, author and subject indices, computer-drawn graphics, and a related Web site The EM Algorithm and Extensions, Second Edition serves as an excellent text for graduate-level statistics students and is also a comprehensive resource for theoreticians, practitioners, and researchers in the social and physical sciences who would like to extend their knowledge of the EM algorithm.