Data Structures, Near Neighbor Searches, and Methodology

Data Structures, Near Neighbor Searches, and Methodology

Author: Michael H. Goldwasser

Publisher: American Mathematical Soc.

Published:

Total Pages: 272

ISBN-13: 9780821871003

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This book presents reviewed and revised papers from the fifth and sixth DIMACS Implementation Challenge workshops. These workshops, held approximately annually, aim at encouraging high-quality work in experimental analysis of data structures and algorithms. The papers published in this volume are the results of year-long coordinated research projects and contain new findings and insights. Three papers address the performance evaluation of implementations for two fundamental data structures, dictionaries and priority queues as used in the context of real applications. Another four papers consider the still evolving topic of methodologies for experimental algorithmics. Five papers are concerned with implementations of algorithms for nearest neighbor search in high dimensional spaces, an area with applications in information retrieval and data mining on collections of Web documents, DNA sequences, images and various other data types.


Book Synopsis Data Structures, Near Neighbor Searches, and Methodology by : Michael H. Goldwasser

Download or read book Data Structures, Near Neighbor Searches, and Methodology written by Michael H. Goldwasser and published by American Mathematical Soc.. This book was released on with total page 272 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents reviewed and revised papers from the fifth and sixth DIMACS Implementation Challenge workshops. These workshops, held approximately annually, aim at encouraging high-quality work in experimental analysis of data structures and algorithms. The papers published in this volume are the results of year-long coordinated research projects and contain new findings and insights. Three papers address the performance evaluation of implementations for two fundamental data structures, dictionaries and priority queues as used in the context of real applications. Another four papers consider the still evolving topic of methodologies for experimental algorithmics. Five papers are concerned with implementations of algorithms for nearest neighbor search in high dimensional spaces, an area with applications in information retrieval and data mining on collections of Web documents, DNA sequences, images and various other data types.

Handbook of Data Structures and Applications

Handbook of Data Structures and Applications

Author: Dinesh P. Mehta

Publisher: Taylor & Francis

Published: 2018-02-21

Total Pages: 2007

ISBN-13: 1351645641

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The Handbook of Data Structures and Applications was first published over a decade ago. This second edition aims to update the first by focusing on areas of research in data structures that have seen significant progress. While the discipline of data structures has not matured as rapidly as other areas of computer science, the book aims to update those areas that have seen advances. Retaining the seven-part structure of the first edition, the handbook begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. Four new chapters have been added on Bloom Filters, Binary Decision Diagrams, Data Structures for Cheminformatics, and Data Structures for Big Data Stores, and updates have been made to other chapters that appeared in the first edition. The Handbook is invaluable for suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.


Book Synopsis Handbook of Data Structures and Applications by : Dinesh P. Mehta

Download or read book Handbook of Data Structures and Applications written by Dinesh P. Mehta and published by Taylor & Francis. This book was released on 2018-02-21 with total page 2007 pages. Available in PDF, EPUB and Kindle. Book excerpt: The Handbook of Data Structures and Applications was first published over a decade ago. This second edition aims to update the first by focusing on areas of research in data structures that have seen significant progress. While the discipline of data structures has not matured as rapidly as other areas of computer science, the book aims to update those areas that have seen advances. Retaining the seven-part structure of the first edition, the handbook begins with a review of introductory material, followed by a discussion of well-known classes of data structures, Priority Queues, Dictionary Structures, and Multidimensional structures. The editors next analyze miscellaneous data structures, which are well-known structures that elude easy classification. The book then addresses mechanisms and tools that were developed to facilitate the use of data structures in real programs. It concludes with an examination of the applications of data structures. Four new chapters have been added on Bloom Filters, Binary Decision Diagrams, Data Structures for Cheminformatics, and Data Structures for Big Data Stores, and updates have been made to other chapters that appeared in the first edition. The Handbook is invaluable for suggesting new ideas for research in data structures, and for revealing application contexts in which they can be deployed. Practitioners devising algorithms will gain insight into organizing data, allowing them to solve algorithmic problems more efficiently.

Data Structures, Near Neighbor Searches, and Methodology

Data Structures, Near Neighbor Searches, and Methodology

Author: Michael H. Goldwasser

Publisher:

Published: 2002

Total Pages: 256

ISBN-13: 9781470440176

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This book presents reviewed and revised papers from the fifth and sixth DIMACS Implementation Challenge workshops. These workshops, held approximately annually, aim at encouraging high-quality work in experimental analysis of data structures and algorithms. The papers published in this volume are the results of year-long coordinated research projects and contain new findings and insights. Three papers address the performance evaluation of implementations for two fundamental data structures, dictionaries and priority queues, as used in the context of real applications. Another four papers consi.


Book Synopsis Data Structures, Near Neighbor Searches, and Methodology by : Michael H. Goldwasser

Download or read book Data Structures, Near Neighbor Searches, and Methodology written by Michael H. Goldwasser and published by . This book was released on 2002 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents reviewed and revised papers from the fifth and sixth DIMACS Implementation Challenge workshops. These workshops, held approximately annually, aim at encouraging high-quality work in experimental analysis of data structures and algorithms. The papers published in this volume are the results of year-long coordinated research projects and contain new findings and insights. Three papers address the performance evaluation of implementations for two fundamental data structures, dictionaries and priority queues, as used in the context of real applications. Another four papers consi.

Handbook of Approximation Algorithms and Metaheuristics

Handbook of Approximation Algorithms and Metaheuristics

Author: Teofilo F. Gonzalez

Publisher: CRC Press

Published: 2007-05-15

Total Pages: 1434

ISBN-13: 1420010743

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Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical applications. It is the first book to comprehensively study both approximation algorithms and metaheuristics. Starting with basic approaches, the handbook presents the methodologies to design and analyze efficient approximation algorithms for a large class of problems, and to establish inapproximability results for another class of problems. It also discusses local search, neural networks, and metaheuristics, as well as multiobjective problems, sensitivity analysis, and stability. After laying this foundation, the book applies the methodologies to classical problems in combinatorial optimization, computational geometry, and graph problems. In addition, it explores large-scale and emerging applications in networks, bioinformatics, VLSI, game theory, and data analysis. Undoubtedly sparking further developments in the field, this handbook provides the essential techniques to apply approximation algorithms and metaheuristics to a wide range of problems in computer science, operations research, computer engineering, and economics. Armed with this information, researchers can design and analyze efficient algorithms to generate near-optimal solutions for a wide range of computational intractable problems.


Book Synopsis Handbook of Approximation Algorithms and Metaheuristics by : Teofilo F. Gonzalez

Download or read book Handbook of Approximation Algorithms and Metaheuristics written by Teofilo F. Gonzalez and published by CRC Press. This book was released on 2007-05-15 with total page 1434 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical applications. It is the first book to comprehensively study both approximation algorithms and metaheuristics. Starting with basic approaches, the handbook presents the methodologies to design and analyze efficient approximation algorithms for a large class of problems, and to establish inapproximability results for another class of problems. It also discusses local search, neural networks, and metaheuristics, as well as multiobjective problems, sensitivity analysis, and stability. After laying this foundation, the book applies the methodologies to classical problems in combinatorial optimization, computational geometry, and graph problems. In addition, it explores large-scale and emerging applications in networks, bioinformatics, VLSI, game theory, and data analysis. Undoubtedly sparking further developments in the field, this handbook provides the essential techniques to apply approximation algorithms and metaheuristics to a wide range of problems in computer science, operations research, computer engineering, and economics. Armed with this information, researchers can design and analyze efficient algorithms to generate near-optimal solutions for a wide range of computational intractable problems.

Mathematical Tools for Data Mining

Mathematical Tools for Data Mining

Author: Dan A. Simovici

Publisher: Springer Science & Business Media

Published: 2014-03-27

Total Pages: 831

ISBN-13: 1447164075

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Data mining essentially relies on several mathematical disciplines, many of which are presented in this second edition of this book. Topics include partially ordered sets, combinatorics, general topology, metric spaces, linear spaces, graph theory. To motivate the reader a significant number of applications of these mathematical tools are included ranging from association rules, clustering algorithms, classification, data constraints, logical data analysis, etc. The book is intended as a reference for researchers and graduate students. The current edition is a significant expansion of the first edition. We strived to make the book self-contained and only a general knowledge of mathematics is required. More than 700 exercises are included and they form an integral part of the material. Many exercises are in reality supplemental material and their solutions are included.


Book Synopsis Mathematical Tools for Data Mining by : Dan A. Simovici

Download or read book Mathematical Tools for Data Mining written by Dan A. Simovici and published by Springer Science & Business Media. This book was released on 2014-03-27 with total page 831 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data mining essentially relies on several mathematical disciplines, many of which are presented in this second edition of this book. Topics include partially ordered sets, combinatorics, general topology, metric spaces, linear spaces, graph theory. To motivate the reader a significant number of applications of these mathematical tools are included ranging from association rules, clustering algorithms, classification, data constraints, logical data analysis, etc. The book is intended as a reference for researchers and graduate students. The current edition is a significant expansion of the first edition. We strived to make the book self-contained and only a general knowledge of mathematics is required. More than 700 exercises are included and they form an integral part of the material. Many exercises are in reality supplemental material and their solutions are included.

Swarm Intelligence for Multi-objective Problems in Data Mining

Swarm Intelligence for Multi-objective Problems in Data Mining

Author: Carlos Coello Coello

Publisher: Springer

Published: 2009-10-01

Total Pages: 287

ISBN-13: 3642036252

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The purpose of this book is to collect contributions that are at the intersection of multi-objective optimization, swarm intelligence (specifically, particle swarm optimization and ant colony optimization) and data mining.


Book Synopsis Swarm Intelligence for Multi-objective Problems in Data Mining by : Carlos Coello Coello

Download or read book Swarm Intelligence for Multi-objective Problems in Data Mining written by Carlos Coello Coello and published by Springer. This book was released on 2009-10-01 with total page 287 pages. Available in PDF, EPUB and Kindle. Book excerpt: The purpose of this book is to collect contributions that are at the intersection of multi-objective optimization, swarm intelligence (specifically, particle swarm optimization and ant colony optimization) and data mining.

Encyclopedia of Algorithms

Encyclopedia of Algorithms

Author: Ming-Yang Kao

Publisher: Springer Science & Business Media

Published: 2008-08-06

Total Pages: 1200

ISBN-13: 0387307702

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One of Springer’s renowned Major Reference Works, this awesome achievement provides a comprehensive set of solutions to important algorithmic problems for students and researchers interested in quickly locating useful information. This first edition of the reference focuses on high-impact solutions from the most recent decade, while later editions will widen the scope of the work. All entries have been written by experts, while links to Internet sites that outline their research work are provided. The entries have all been peer-reviewed. This defining reference is published both in print and on line.


Book Synopsis Encyclopedia of Algorithms by : Ming-Yang Kao

Download or read book Encyclopedia of Algorithms written by Ming-Yang Kao and published by Springer Science & Business Media. This book was released on 2008-08-06 with total page 1200 pages. Available in PDF, EPUB and Kindle. Book excerpt: One of Springer’s renowned Major Reference Works, this awesome achievement provides a comprehensive set of solutions to important algorithmic problems for students and researchers interested in quickly locating useful information. This first edition of the reference focuses on high-impact solutions from the most recent decade, while later editions will widen the scope of the work. All entries have been written by experts, while links to Internet sites that outline their research work are provided. The entries have all been peer-reviewed. This defining reference is published both in print and on line.

Mathematical Programming Solver Based on Local Search

Mathematical Programming Solver Based on Local Search

Author: Frédéric Gardi

Publisher: John Wiley & Sons

Published: 2014-07-09

Total Pages: 82

ISBN-13: 1118966481

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This book covers local search for combinatorial optimization and its extension to mixed-variable optimization. Although not yet understood from the theoretical point of view, local search is the paradigm of choice for tackling large-scale real-life optimization problems. Today's end-users demand interactivity with decision support systems. For optimization software, this means obtaining good-quality solutions quickly. Fast iterative improvement methods, like local search, are suited to satisfying such needs. Here the authors show local search in a new light, in particular presenting a new kind of mathematical programming solver, namely LocalSolver, based on neighborhood search. First, an iconoclast methodology is presented to design and engineer local search algorithms. The authors' concern regarding industrializing local search approaches is of particular interest for practitioners. This methodology is applied to solve two industrial problems with high economic stakes. Software based on local search induces extra costs in development and maintenance in comparison with the direct use of mixed-integer linear programming solvers. The authors then move on to present the LocalSolver project whose goal is to offer the power of local search through a model-and-run solver for large-scale 0-1 nonlinear programming. They conclude by presenting their ongoing and future work on LocalSolver toward a full mathematical programming solver based on local search.


Book Synopsis Mathematical Programming Solver Based on Local Search by : Frédéric Gardi

Download or read book Mathematical Programming Solver Based on Local Search written by Frédéric Gardi and published by John Wiley & Sons. This book was released on 2014-07-09 with total page 82 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book covers local search for combinatorial optimization and its extension to mixed-variable optimization. Although not yet understood from the theoretical point of view, local search is the paradigm of choice for tackling large-scale real-life optimization problems. Today's end-users demand interactivity with decision support systems. For optimization software, this means obtaining good-quality solutions quickly. Fast iterative improvement methods, like local search, are suited to satisfying such needs. Here the authors show local search in a new light, in particular presenting a new kind of mathematical programming solver, namely LocalSolver, based on neighborhood search. First, an iconoclast methodology is presented to design and engineer local search algorithms. The authors' concern regarding industrializing local search approaches is of particular interest for practitioners. This methodology is applied to solve two industrial problems with high economic stakes. Software based on local search induces extra costs in development and maintenance in comparison with the direct use of mixed-integer linear programming solvers. The authors then move on to present the LocalSolver project whose goal is to offer the power of local search through a model-and-run solver for large-scale 0-1 nonlinear programming. They conclude by presenting their ongoing and future work on LocalSolver toward a full mathematical programming solver based on local search.

Stochastic Local Search

Stochastic Local Search

Author: Holger H. Hoos

Publisher: Morgan Kaufmann

Published: 2005

Total Pages: 678

ISBN-13: 1558608729

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Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems. Offering a systematic treatment of SLS algorithms, this book examines the general concepts and specific instances of SLS algorithms and considers their development, analysis and application.


Book Synopsis Stochastic Local Search by : Holger H. Hoos

Download or read book Stochastic Local Search written by Holger H. Hoos and published by Morgan Kaufmann. This book was released on 2005 with total page 678 pages. Available in PDF, EPUB and Kindle. Book excerpt: Stochastic local search (SLS) algorithms are among the most prominent and successful techniques for solving computationally difficult problems. Offering a systematic treatment of SLS algorithms, this book examines the general concepts and specific instances of SLS algorithms and considers their development, analysis and application.

Author:

Publisher: IOS Press

Published:

Total Pages: 7289

ISBN-13:

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Book Synopsis by :

Download or read book written by and published by IOS Press. This book was released on with total page 7289 pages. Available in PDF, EPUB and Kindle. Book excerpt: