Graph Based Clustering And Data Visualization Algorithms

Author : Ágnes Vathy-Fogarassy,János Abonyi
Publisher : Springer Science & Business Media
Page : 120 pages
File Size : 55,6 Mb
Release : 2013-05-24
Category : Computers
ISBN : 9781447151586

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Graph-Based Clustering and Data Visualization Algorithms by Ágnes Vathy-Fogarassy,János Abonyi Pdf

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.

Author : Ágnes Vathy-Fogarassy,Janos Abonyi
Publisher : Unknown
Page : 126 pages
File Size : 48,5 Mb
Release : 2013-06-30
Category : Electronic
ISBN : 1447151593

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Graph-Based Clustering and Data Visualization Algorithms by Ágnes Vathy-Fogarassy,Janos Abonyi Pdf

Author : Charu C. Aggarwal,Chandan K. Reddy
Publisher : CRC Press
Page : 654 pages
File Size : 44,5 Mb
Release : 2018-09-03
Category : Business & Economics
ISBN : 9781315360416

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Data Clustering by Charu C. Aggarwal,Chandan K. Reddy Pdf

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.

Author : Guojun Gan,Chaoqun Ma,Jianhong Wu
Publisher : SIAM
Page : 430 pages
File Size : 42,6 Mb
Release : 2020-11-10
Category : Mathematics
ISBN : 9781611976335

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Data Clustering: Theory, Algorithms, and Applications, Second Edition by Guojun Gan,Chaoqun Ma,Jianhong Wu Pdf

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.

Author : Sascha Hunold,Alexandru Costan,Domingo Giménez,Alexandru Iosup,Laura Ricci,María Engracia Gómez Requena,Vittorio Scarano,Ana Lucia Varbanescu,Stephen L. Scott,Stefan Lankes,Josef Weidendorfer,Michael Alexander
Publisher : Springer
Page : 839 pages
File Size : 44,8 Mb
Release : 2015-12-17
Category : Computers
ISBN : 9783319273082

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Euro-Par 2015: Parallel Processing Workshops by Sascha Hunold,Alexandru Costan,Domingo Giménez,Alexandru Iosup,Laura Ricci,María Engracia Gómez Requena,Vittorio Scarano,Ana Lucia Varbanescu,Stephen L. Scott,Stefan Lankes,Josef Weidendorfer,Michael Alexander Pdf

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.

Author : François Fouss,Marco Saerens,Masashi Shimbo
Publisher : Cambridge University Press
Page : 549 pages
File Size : 51,8 Mb
Release : 2016-07-12
Category : Computers
ISBN : 9781107125773

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Algorithms and Models for Network Data and Link Analysis by François Fouss,Marco Saerens,Masashi Shimbo Pdf

A hands-on, entry-level guide to algorithms for extracting information about social and economic behavior from network data.

Author : Adam Schenker,Abraham Kandel,Horst Bunke,Mark Last
Publisher : World Scientific
Page : 248 pages
File Size : 41,5 Mb
Release : 2005-05-31
Category : Computers
ISBN : 9789814480345

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Graph-Theoretic Techniques for Web Content Mining by Adam Schenker,Abraham Kandel,Horst Bunke,Mark Last Pdf

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

Author : Slawomir Wierzchoń,Mieczyslaw Kłopotek
Publisher : Springer
Page : 421 pages
File Size : 47,7 Mb
Release : 2017-12-29
Category : Technology & Engineering
ISBN : 9783319693088

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Modern Algorithms of Cluster Analysis by Slawomir Wierzchoń,Mieczyslaw Kłopotek Pdf

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.

Author : Charu C. Aggarwal,Haixun Wang
Publisher : Springer Science & Business Media
Page : 623 pages
File Size : 49,9 Mb
Release : 2010-02-02
Category : Computers
ISBN : 9781441960450

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Managing and Mining Graph Data by Charu C. Aggarwal,Haixun Wang Pdf

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.

Author : Chee-Peng Lim,Yen-Wei Chen,Ashlesha Vaidya,Charu Mahorkar,Lakhmi C. Jain
Publisher : Springer Nature
Page : 429 pages
File Size : 51,8 Mb
Release : 2021-11-26
Category : Technology & Engineering
ISBN : 9783030836207

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Handbook of Artificial Intelligence in Healthcare by Chee-Peng Lim,Yen-Wei Chen,Ashlesha Vaidya,Charu Mahorkar,Lakhmi C. Jain Pdf

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..

Author : Alessandro Negro
Publisher : Simon and Schuster
Page : 494 pages
File Size : 54,6 Mb
Release : 2021-10-05
Category : Computers
ISBN : 9781638353935

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Graph-Powered Machine Learning by Alessandro Negro Pdf

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

Author : Management Association, Information Resources
Publisher : IGI Global
Page : 530 pages
File Size : 48,7 Mb
Release : 2017-03-03
Category : Medical
ISBN : 9781522519041

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

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.

Author : Anonim
Publisher : Elsevier
Page : 660 pages
File Size : 44,9 Mb
Release : 2005-05-02
Category : Mathematics
ISBN : 9780080459400

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Data Mining and Data Visualization by Anonim Pdf

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

Author : Francisco Escolano,Mario Vento
Publisher : Springer
Page : 416 pages
File Size : 52,6 Mb
Release : 2007-08-20
Category : Computers
ISBN : 9783540729037

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Graph-Based Representations in Pattern Recognition by Francisco Escolano,Mario Vento Pdf

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.

Author : Petia Georgieva,Lyudmila Mihaylova,Lakhmi C Jain
Publisher : Springer
Page : 359 pages
File Size : 48,6 Mb
Release : 2012-07-27
Category : Technology & Engineering
ISBN : 9783642286964

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Advances in Intelligent Signal Processing and Data Mining by Petia Georgieva,Lyudmila Mihaylova,Lakhmi C Jain Pdf

The book presents some of the most efficient statistical and deterministic methods for information processing and applications in order to extract targeted information and find hidden patterns. The techniques presented range from Bayesian approaches and their variations such as sequential Monte Carlo methods, Markov Chain Monte Carlo filters, Rao Blackwellization, to the biologically inspired paradigm of Neural Networks and decomposition techniques such as Empirical Mode Decomposition, Independent Component Analysis and Singular Spectrum Analysis. The book is directed to the research students, professors, researchers and practitioners interested in exploring the advanced techniques in intelligent signal processing and data mining paradigms.