Graph Based Clustering And Data Visualization Algorithms

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Graph-Based Clustering and Data Visualization Algorithms

Author : Ágnes Vathy-Fogarassy
Publisher : Springer Science & Business Media
ISBN 13 : 1447151585
Total Pages : 110 pages
Book Rating : 4.4/5 (471 download)

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Book Synopsis Graph-Based Clustering and Data Visualization Algorithms by : Ágnes Vathy-Fogarassy

Download or read book Graph-Based Clustering and Data Visualization Algorithms written by Ágnes Vathy-Fogarassy and published by Springer Science & Business Media. This book was released on 2013-05-24 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.

Graph-Based Clustering and Data Visualization Algorithms

Author : Ágnes Vathy-Fogarassy
Publisher :
ISBN 13 : 9781447151593
Total Pages : 126 pages
Book Rating : 4.1/5 (515 download)

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Book Synopsis Graph-Based Clustering and Data Visualization Algorithms by : Ágnes Vathy-Fogarassy

Download or read book Graph-Based Clustering and Data Visualization Algorithms written by Ágnes Vathy-Fogarassy and published by . This book was released on 2013-06-30 with total page 126 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Data Clustering: Theory, Algorithms, and Applications, Second Edition

Author : Guojun Gan
Publisher : SIAM
ISBN 13 : 1611976332
Total Pages : 430 pages
Book Rating : 4.6/5 (119 download)

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Book Synopsis Data Clustering: Theory, Algorithms, and Applications, Second Edition by : Guojun Gan

Download or read book Data Clustering: Theory, Algorithms, and Applications, Second Edition written by Guojun Gan and published by SIAM. This book was released on 2020-11-10 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data clustering, also known as cluster analysis, is an unsupervised process that divides a set of objects into homogeneous groups. Since the publication of the first edition of this monograph in 2007, development in the area has exploded, especially in clustering algorithms for big data and open-source software for cluster analysis. This second edition reflects these new developments, covers the basics of data clustering, includes a list of popular clustering algorithms, and provides program code that helps users implement clustering algorithms. Data Clustering: Theory, Algorithms and Applications, Second Edition will be of interest to researchers, practitioners, and data scientists as well as undergraduate and graduate students.

Managing and Mining Graph Data

Author : Charu C. Aggarwal
Publisher : Springer Science & Business Media
ISBN 13 : 1441960457
Total Pages : 623 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Managing and Mining Graph Data by : Charu C. Aggarwal

Download or read book Managing and Mining Graph Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2010-02-02 with total page 623 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing and Mining Graph Data is a comprehensive survey book in graph management and mining. It contains extensive surveys on a variety of important graph topics such as graph languages, indexing, clustering, data generation, pattern mining, classification, keyword search, pattern matching, and privacy. It also studies a number of domain-specific scenarios such as stream mining, web graphs, social networks, chemical and biological data. The chapters are written by well known researchers in the field, and provide a broad perspective of the area. This is the first comprehensive survey book in the emerging topic of graph data processing. Managing and Mining Graph Data is designed for a varied audience composed of professors, researchers and practitioners in industry. This volume is also suitable as a reference book for advanced-level database students in computer science and engineering.

Data Clustering

Author : Charu C. Aggarwal
Publisher : CRC Press
ISBN 13 : 1315360411
Total Pages : 654 pages
Book Rating : 4.3/5 (153 download)

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Book Synopsis Data Clustering by : Charu C. Aggarwal

Download or read book Data Clustering written by Charu C. Aggarwal and published by CRC Press. This book was released on 2018-09-03 with total page 654 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains. The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as feature selection, agglomerative clustering, partitional clustering, density-based clustering, probabilistic clustering, grid-based clustering, spectral clustering, and nonnegative matrix factorization Domains, covering methods used for different domains of data, such as categorical data, text data, multimedia data, graph data, biological data, stream data, uncertain data, time series clustering, high-dimensional clustering, and big data Variations and Insights, discussing important variations of the clustering process, such as semisupervised clustering, interactive clustering, multiview clustering, cluster ensembles, and cluster validation In this book, top researchers from around the world explore the characteristics of clustering problems in a variety of application areas. They also explain how to glean detailed insight from the clustering process—including how to verify the quality of the underlying clusters—through supervision, human intervention, or the automated generation of alternative clusters.

Graph-Powered Machine Learning

Author : Alessandro Negro
Publisher : Simon and Schuster
ISBN 13 : 163835393X
Total Pages : 494 pages
Book Rating : 4.6/5 (383 download)

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Book Synopsis Graph-Powered Machine Learning by : Alessandro Negro

Download or read book Graph-Powered Machine Learning written by Alessandro Negro and published by Simon and Schuster. This book was released on 2021-10-05 with total page 494 pages. Available in PDF, EPUB and Kindle. Book excerpt: Upgrade your machine learning models with graph-based algorithms, the perfect structure for complex and interlinked data. Summary In Graph-Powered Machine Learning, you will learn: The lifecycle of a machine learning project Graphs in big data platforms Data source modeling using graphs Graph-based natural language processing, recommendations, and fraud detection techniques Graph algorithms Working with Neo4J Graph-Powered Machine Learning teaches to use graph-based algorithms and data organization strategies to develop superior machine learning applications. You’ll dive into the role of graphs in machine learning and big data platforms, and take an in-depth look at data source modeling, algorithm design, recommendations, and fraud detection. Explore end-to-end projects that illustrate architectures and help you optimize with best design practices. Author Alessandro Negro’s extensive experience shines through in every chapter, as you learn from examples and concrete scenarios based on his work with real clients! Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Identifying relationships is the foundation of machine learning. By recognizing and analyzing the connections in your data, graph-centric algorithms like K-nearest neighbor or PageRank radically improve the effectiveness of ML applications. Graph-based machine learning techniques offer a powerful new perspective for machine learning in social networking, fraud detection, natural language processing, and recommendation systems. About the book Graph-Powered Machine Learning teaches you how to exploit the natural relationships in structured and unstructured datasets using graph-oriented machine learning algorithms and tools. In this authoritative book, you’ll master the architectures and design practices of graphs, and avoid common pitfalls. Author Alessandro Negro explores examples from real-world applications that connect GraphML concepts to real world tasks. What's inside Graphs in big data platforms Recommendations, natural language processing, fraud detection Graph algorithms Working with the Neo4J graph database About the reader For readers comfortable with machine learning basics. About the author Alessandro Negro is Chief Scientist at GraphAware. He has been a speaker at many conferences, and holds a PhD in Computer Science. Table of Contents PART 1 INTRODUCTION 1 Machine learning and graphs: An introduction 2 Graph data engineering 3 Graphs in machine learning applications PART 2 RECOMMENDATIONS 4 Content-based recommendations 5 Collaborative filtering 6 Session-based recommendations 7 Context-aware and hybrid recommendations PART 3 FIGHTING FRAUD 8 Basic approaches to graph-powered fraud detection 9 Proximity-based algorithms 10 Social network analysis against fraud PART 4 TAMING TEXT WITH GRAPHS 11 Graph-based natural language processing 12 Knowledge graphs

Graph Algorithms

Author : Mark Needham
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492047635
Total Pages : 297 pages
Book Rating : 4.4/5 (92 download)

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Book Synopsis Graph Algorithms by : Mark Needham

Download or read book Graph Algorithms written by Mark Needham and published by "O'Reilly Media, Inc.". This book was released on 2019-05-16 with total page 297 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover how graph algorithms can help you leverage the relationships within your data to develop more intelligent solutions and enhance your machine learning models. You’ll learn how graph analytics are uniquely suited to unfold complex structures and reveal difficult-to-find patterns lurking in your data. Whether you are trying to build dynamic network models or forecast real-world behavior, this book illustrates how graph algorithms deliver value—from finding vulnerabilities and bottlenecks to detecting communities and improving machine learning predictions. This practical book walks you through hands-on examples of how to use graph algorithms in Apache Spark and Neo4j—two of the most common choices for graph analytics. Also included: sample code and tips for over 20 practical graph algorithms that cover optimal pathfinding, importance through centrality, and community detection. Learn how graph analytics vary from conventional statistical analysis Understand how classic graph algorithms work, and how they are applied Get guidance on which algorithms to use for different types of questions Explore algorithm examples with working code and sample datasets from Spark and Neo4j See how connected feature extraction can increase machine learning accuracy and precision Walk through creating an ML workflow for link prediction combining Neo4j and Spark

Euro-Par 2015: Parallel Processing Workshops

Author : Sascha Hunold
Publisher : Springer
ISBN 13 : 3319273086
Total Pages : 839 pages
Book Rating : 4.3/5 (192 download)

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Book Synopsis Euro-Par 2015: Parallel Processing Workshops by : Sascha Hunold

Download or read book Euro-Par 2015: Parallel Processing Workshops written by Sascha Hunold and published by Springer. This book was released on 2015-12-17 with total page 839 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the thoroughly refereed post-conference proceedings of 12 workshops held at the 21st International Conference on Parallel and Distributed Computing, Euro-Par 2015, in Vienna, Austria, in August 2015. The 67 revised full papers presented were carefully reviewed and selected from 121 submissions. The volume includes papers from the following workshops: BigDataCloud: 4th Workshop on Big Data Management in Clouds - Euro-EDUPAR: First European Workshop on Parallel and Distributed Computing Education for Undergraduate Students - Hetero Par: 13th International Workshop on Algorithms, Models and Tools for Parallel Computing on Heterogeneous Platforms - LSDVE: Third Workshop on Large Scale Distributed Virtual Environments - OMHI: 4th International Workshop on On-chip Memory Hierarchies and Interconnects - PADAPS: Third Workshop on Parallel and Distributed Agent-Based Simulations - PELGA: Workshop on Performance Engineering for Large-Scale Graph Analytics - REPPAR: Second International Workshop on Reproducibility in Parallel Computing - Resilience: 8th Workshop on Resiliency in High Performance Computing in Clusters, Clouds, and Grids - ROME: Third Workshop on Runtime and Operating Systems for the Many Core Era - UCHPC: 8th Workshop on UnConventional High Performance Computing - and VHPC: 10th Workshop on Virtualization in High-Performance Cloud Computing.

Graph Algorithms for Data Science

Author : Tomaž Bratanic
Publisher : Simon and Schuster
ISBN 13 : 1617299464
Total Pages : 350 pages
Book Rating : 4.6/5 (172 download)

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Book Synopsis Graph Algorithms for Data Science by : Tomaž Bratanic

Download or read book Graph Algorithms for Data Science written by Tomaž Bratanic and published by Simon and Schuster. This book was released on 2024-02-27 with total page 350 pages. Available in PDF, EPUB and Kindle. Book excerpt: Graph Algorithms for Data Science teaches you how to construct graphs from both structured and unstructured data. You'll learn how the flexible Cypher query language can be used to easily manipulate graph structures, and extract amazing insights. Graph Algorithms for Data Science is a hands-on guide to working with graph-based data in applications. It's filled with fascinating and fun projects, demonstrating the ins-and-outs of graphs. You'll gain practical skills by analyzing Twitter, building graphs with NLP techniques, and much more. These powerful graph algorithms are explained in clear, jargon-free text and illustrations that makes them easy to apply to your own projects.

Algorithms and Models for Network Data and Link Analysis

Author : François Fouss
Publisher : Cambridge University Press
ISBN 13 : 1107125774
Total Pages : 549 pages
Book Rating : 4.1/5 (71 download)

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Book Synopsis Algorithms and Models for Network Data and Link Analysis by : François Fouss

Download or read book Algorithms and Models for Network Data and Link Analysis written by François Fouss and published by Cambridge University Press. This book was released on 2016-07-12 with total page 549 pages. Available in PDF, EPUB and Kindle. Book excerpt: A hands-on, entry-level guide to algorithms for extracting information about social and economic behavior from network data.

Graph-Theoretic Techniques for Web Content Mining

Author : Adam Schenker
Publisher : World Scientific
ISBN 13 : 9814480347
Total Pages : 248 pages
Book Rating : 4.8/5 (144 download)

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Book Synopsis Graph-Theoretic Techniques for Web Content Mining by : Adam Schenker

Download or read book Graph-Theoretic Techniques for Web Content Mining written by Adam Schenker and published by World Scientific. This book was released on 2005-05-31 with total page 248 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes exciting new opportunities for utilizing robust graph representations of data with common machine learning algorithms. Graphs can model additional information which is often not present in commonly used data representations, such as vectors. Through the use of graph distance — a relatively new approach for determining graph similarity — the authors show how well-known algorithms, such as k-means clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization of additional information found in graph representations, while at the same time employing well-known, proven algorithms. To demonstrate and investigate these novel techniques, the authors have selected the domain of web content mining, which involves the clustering and classification of web documents based on their textual substance. Several methods of representing web document content by graphs are introduced; an interesting feature of these representations is that they allow for a polynomial time distance computation, something which is typically an NP-complete problem when using graphs. Experimental results are reported for both clustering and classification in three web document collections using a variety of graph representations, distance measures, and algorithm parameters. In addition, this book describes several other related topics, many of which provide excellent starting points for researchers and students interested in exploring this new area of machine learning further. These topics include creating graph-based multiple classifier ensembles through random node selection and visualization of graph-based data using multidimensional scaling. Contents:Introduction to Web MiningGraph Similarity TechniquesGraph Models for Web DocumentsGraph-Based ClusteringGraph-Based ClassificationThe Graph Hierarchy Construction Algorithm for Web Search Clustering Readership: Researchers and graduate students who are interested in computer science, specifically machine learning. Also of interest to researchers in academia or industry in disciplines such as information science or information technology who are interested in text and web documents. Keywords:Graph;Machine Learning;Web Mining;Data Mining;Clustering;Classification;Graph Distance;Maximum Common SubgraphKey Features:Opens up exciting new possibilities for utilizing graphs in common machine learning algorithmsPresents experimental results comparing differing graph representations and graph distance measuresProvides a review of graph-theoretic similarity techniques

Modern Algorithms of Cluster Analysis

Author : Slawomir Wierzchoń
Publisher : Springer
ISBN 13 : 3319693085
Total Pages : 421 pages
Book Rating : 4.3/5 (196 download)

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Book Synopsis Modern Algorithms of Cluster Analysis by : Slawomir Wierzchoń

Download or read book Modern Algorithms of Cluster Analysis written by Slawomir Wierzchoń and published by Springer. This book was released on 2017-12-29 with total page 421 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides the reader with a basic understanding of the formal concepts of the cluster, clustering, partition, cluster analysis etc. The book explains feature-based, graph-based and spectral clustering methods and discusses their formal similarities and differences. Understanding the related formal concepts is particularly vital in the epoch of Big Data; due to the volume and characteristics of the data, it is no longer feasible to predominantly rely on merely viewing the data when facing a clustering problem. Usually clustering involves choosing similar objects and grouping them together. To facilitate the choice of similarity measures for complex and big data, various measures of object similarity, based on quantitative (like numerical measurement results) and qualitative features (like text), as well as combinations of the two, are described, as well as graph-based similarity measures for (hyper) linked objects and measures for multilayered graphs. Numerous variants demonstrating how such similarity measures can be exploited when defining clustering cost functions are also presented. In addition, the book provides an overview of approaches to handling large collections of objects in a reasonable time. In particular, it addresses grid-based methods, sampling methods, parallelization via Map-Reduce, usage of tree-structures, random projections and various heuristic approaches, especially those used for community detection.

Data Mining and Data Visualization

Author :
Publisher : Elsevier
ISBN 13 : 9780080459400
Total Pages : 800 pages
Book Rating : 4.4/5 (594 download)

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Book Synopsis Data Mining and Data Visualization by :

Download or read book Data Mining and Data Visualization written by and published by Elsevier. This book was released on 2005-05-02 with total page 800 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining and Data Visualization focuses on dealing with large-scale data, a field commonly referred to as data mining. The book is divided into three sections. The first deals with an introduction to statistical aspects of data mining and machine learning and includes applications to text analysis, computer intrusion detection, and hiding of information in digital files. The second section focuses on a variety of statistical methodologies that have proven to be effective in data mining applications. These include clustering, classification, multivariate density estimation, tree-based methods, pattern recognition, outlier detection, genetic algorithms, and dimensionality reduction. The third section focuses on data visualization and covers issues of visualization of high-dimensional data, novel graphical techniques with a focus on human factors, interactive graphics, and data visualization using virtual reality. This book represents a thorough cross section of internationally renowned thinkers who are inventing methods for dealing with a new data paradigm. Distinguished contributors who are international experts in aspects of data mining Includes data mining approaches to non-numerical data mining including text data, Internet traffic data, and geographic data Highly topical discussions reflecting current thinking on contemporary technical issues, e.g. streaming data Discusses taxonomy of dataset sizes, computational complexity, and scalability usually ignored in most discussions Thorough discussion of data visualization issues blending statistical, human factors, and computational insights

Handbook of Artificial Intelligence in Healthcare

Author : Chee-Peng Lim
Publisher : Springer Nature
ISBN 13 : 3030836207
Total Pages : 429 pages
Book Rating : 4.0/5 (38 download)

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Book Synopsis Handbook of Artificial Intelligence in Healthcare by : Chee-Peng Lim

Download or read book Handbook of Artificial Intelligence in Healthcare written by Chee-Peng Lim and published by Springer Nature. This book was released on 2021-11-26 with total page 429 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) has transformed many aspects of our daily activities. Health and well-being of humans stand as one of the key domains where AI has achieved significant progresses, saving time, costs, and potentially lives, as well as fostering economic resilience, particularly under the COVID-19 pandemic environments. This book is a sequel of the Handbook of Artificial Intelligence in Healthcare. The first volume of the Handbook is dedicated to present advances and applications of AI methodologies in several specific areas, i.e., signal, image, and video processing as well as information and data analytics. In this second volume of the Handbook, general practicality challenges and future prospects of AI methodologies pertaining to healthcare and related domains are presented in Part 1 and Part 2, respectively. It is envisaged that the selected studies will provide readers a general perspective on the issues, challenges, and opportunities in designing, developing, and implementing AI-based tools and solutions in the healthcare sector, bringing benefits to transform and advance health and well-being development of humans..

Applications of Fuzzy Sets Theory

Author : Francesco Masulli
Publisher : Springer Science & Business Media
ISBN 13 : 354073399X
Total Pages : 708 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Applications of Fuzzy Sets Theory by : Francesco Masulli

Download or read book Applications of Fuzzy Sets Theory written by Francesco Masulli and published by Springer Science & Business Media. This book was released on 2007-06-29 with total page 708 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 7th International Workshop on Fuzzy Logic and Applications, held in Camogli, Italy in July 2007, presented the latest findings in the field. This volume features the refereed proceedings from that meeting. It includes 84 full papers as well as three keynote speeches. The papers are organized into topical sections covering fuzzy set theory, fuzzy information access and retrieval, fuzzy machine learning, and fuzzy architectures and systems.

Oral Healthcare and Technologies: Breakthroughs in Research and Practice

Author : Management Association, Information Resources
Publisher : IGI Global
ISBN 13 : 1522519041
Total Pages : 530 pages
Book Rating : 4.5/5 (225 download)

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Book Synopsis Oral Healthcare and Technologies: Breakthroughs in Research and Practice by : Management Association, Information Resources

Download or read book Oral Healthcare and Technologies: Breakthroughs in Research and Practice written by Management Association, Information Resources and published by IGI Global. This book was released on 2017-03-03 with total page 530 pages. Available in PDF, EPUB and Kindle. Book excerpt: Emerging innovations in the medical sector have created new opportunities for improved patient care and disease control. By optimizing current practices and procedures, improvements in healthcare delivery and quality can be achieved. Oral Healthcare and Technologies: Breakthroughs in Research and Practice is a comprehensive resource with the latest scholarly perspectives on the technological advancements and real-world applications for oral hygiene and medical care. Featuring extensive coverage across a range of relevant perspectives and topics, such as disease management, healthcare administration, and medical informatics, this multi-volume book is ideally designed for professionals, researchers, students, and practitioners seeking academic material on developments and innovations in oral medicine.

Graph-Based Representations in Pattern Recognition

Author : Francisco Escolano
Publisher : Springer
ISBN 13 : 3540729038
Total Pages : 416 pages
Book Rating : 4.5/5 (47 download)

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Book Synopsis Graph-Based Representations in Pattern Recognition by : Francisco Escolano

Download or read book Graph-Based Representations in Pattern Recognition written by Francisco Escolano and published by Springer. This book was released on 2007-08-20 with total page 416 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the 6th IAPR-TC-15 International Workshop on Graph-Based Representations in Pattern Recognition, GbRPR 2007, held in Alicante, Spain in June 2007. It covers matching, distances and measures, graph-based segmentation and image processing, graph-based clustering, graph representations, pyramids, combinatorial maps and homologies, as well as graph clustering, embedding and learning.