Mining Heterogeneous Information Networks

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Publisher : Morgan & Claypool Publishers
ISBN 13 : 1608458806
Total Pages : 162 pages
Book Rating : 4.6/5 (84 download)

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Book Synopsis Mining Heterogeneous Information Networks by : Yizhou Sun

Download or read book Mining Heterogeneous Information Networks written by Yizhou Sun and published by Morgan & Claypool Publishers. This book was released on 2012 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt: Investigates the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, the semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network.

Mining Heterogeneous Information Networks

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Author :
Publisher : Springer Nature
ISBN 13 : 3031019024
Total Pages : 196 pages
Book Rating : 4.0/5 (31 download)

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Book Synopsis Mining Heterogeneous Information Networks by : Yizhou Sun

Download or read book Mining Heterogeneous Information Networks written by Yizhou Sun and published by Springer Nature. This book was released on 2022-05-31 with total page 196 pages. Available in PDF, EPUB and Kindle. Book excerpt: Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this book, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: (1) rank-based clustering and classification; (2) meta-path-based similarity search and mining; (3) relation strength-aware mining, and many other potential developments. This book introduces this new research frontier and points out some promising research directions. Table of Contents: Introduction / Ranking-Based Clustering / Classification of Heterogeneous Information Networks / Meta-Path-Based Similarity Search / Meta-Path-Based Relationship Prediction / Relation Strength-Aware Clustering with Incomplete Attributes / User-Guided Clustering via Meta-Path Selection / Research Frontiers

Heterogeneous Information Network Analysis and Applications

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Publisher : Springer
ISBN 13 : 3319562126
Total Pages : 233 pages
Book Rating : 4.3/5 (195 download)

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Book Synopsis Heterogeneous Information Network Analysis and Applications by : Chuan Shi

Download or read book Heterogeneous Information Network Analysis and Applications written by Chuan Shi and published by Springer. This book was released on 2017-05-25 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation. This information will help researchers to understand how to analyze networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data. Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking or pattern recognition.

Discovery Science

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Publisher : Springer
ISBN 13 : 3642047475
Total Pages : 487 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Discovery Science by : João Gama

Download or read book Discovery Science written by João Gama and published by Springer. This book was released on 2009-10-07 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the twelfth International Conference, on Discovery Science, DS 2009, held in Porto, Portugal, in October 2009. The 35 revised full papers presented were carefully selected from 92 papers. The scope of the conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their applications.

Network Embedding

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Publisher : Morgan & Claypool Publishers
ISBN 13 : 1636390455
Total Pages : 244 pages
Book Rating : 4.6/5 (363 download)

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Book Synopsis Network Embedding by : Cheng Yang

Download or read book Network Embedding written by Cheng Yang and published by Morgan & Claypool Publishers. This book was released on 2021-03-25 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt: This is a comprehensive introduction to the basic concepts, models, and applications of network representation learning (NRL) and the background and rise of network embeddings (NE). It introduces the development of NE techniques by presenting several representative methods on general graphs, as well as a unified NE framework based on matrix factorization. Afterward, it presents the variants of NE with additional information: NE for graphs with node attributes/contents/labels; and the variants with different characteristics: NE for community-structured/large-scale/heterogeneous graphs. Further, the book introduces different applications of NE such as recommendation and information diffusion prediction. Finally, the book concludes the methods and applications and looks forward to the future directions. Many machine learning algorithms require real-valued feature vectors of data instances as inputs. By projecting data into vector spaces, representation learning techniques have achieved promising performance in many areas such as computer vision and natural language processing. There is also a need to learn representations for discrete relational data, namely networks or graphs. Network Embedding (NE) aims at learning vector representations for each node or vertex in a network to encode the topologic structure. Due to its convincing performance and efficiency, NE has been widely applied in many network applications such as node classification and link prediction.

Link Mining: Models, Algorithms, and Applications

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Publisher : Springer Science & Business Media
ISBN 13 : 1441965157
Total Pages : 580 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Link Mining: Models, Algorithms, and Applications by : Philip S. Yu

Download or read book Link Mining: Models, Algorithms, and Applications written by Philip S. Yu and published by Springer Science & Business Media. This book was released on 2010-09-16 with total page 580 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers detailed surveys and systematic discussion of models, algorithms and applications for link mining, focusing on theory and technique, and related applications: text mining, social network analysis, collaborative filtering and bioinformatics.

Mining Text Data

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Publisher : Springer Science & Business Media
ISBN 13 : 1461432235
Total Pages : 527 pages
Book Rating : 4.4/5 (614 download)

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Book Synopsis Mining Text Data by : Charu C. Aggarwal

Download or read book Mining Text Data written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2012-02-03 with total page 527 pages. Available in PDF, EPUB and Kindle. Book excerpt: Text mining applications have experienced tremendous advances because of web 2.0 and social networking applications. Recent advances in hardware and software technology have lead to a number of unique scenarios where text mining algorithms are learned. Mining Text Data introduces an important niche in the text analytics field, and is an edited volume contributed by leading international researchers and practitioners focused on social networks & data mining. This book contains a wide swath in topics across social networks & data mining. Each chapter contains a comprehensive survey including the key research content on the topic, and the future directions of research in the field. There is a special focus on Text Embedded with Heterogeneous and Multimedia Data which makes the mining process much more challenging. A number of methods have been designed such as transfer learning and cross-lingual mining for such cases. Mining Text Data simplifies the content, so that advanced-level students, practitioners and researchers in computer science can benefit from this book. Academic and corporate libraries, as well as ACM, IEEE, and Management Science focused on information security, electronic commerce, databases, data mining, machine learning, and statistics are the primary buyers for this reference book.

Data Mining in Dynamic Social Networks and Fuzzy Systems

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Publisher : IGI Global
ISBN 13 : 1466642149
Total Pages : 412 pages
Book Rating : 4.4/5 (666 download)

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Book Synopsis Data Mining in Dynamic Social Networks and Fuzzy Systems by : Bhatnagar, Vishal

Download or read book Data Mining in Dynamic Social Networks and Fuzzy Systems written by Bhatnagar, Vishal and published by IGI Global. This book was released on 2013-06-30 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: Many organizations, whether in the public or private sector, have begun to take advantage of the tools and techniques used for data mining. Utilizing data mining tools, these organizations are able to reveal the hidden and unknown information from available data. Data Mining in Dynamic Social Networks and Fuzzy Systems brings together research on the latest trends and patterns of data mining tools and techniques in dynamic social networks and fuzzy systems. With these improved modern techniques of data mining, this publication aims to provide insight and support to researchers and professionals concerned with the management of expertise, knowledge, information, and organizational development.

Network Data Mining And Analysis

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Publisher : World Scientific
ISBN 13 : 9813274972
Total Pages : 205 pages
Book Rating : 4.8/5 (132 download)

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Book Synopsis Network Data Mining And Analysis by : Ming Gao

Download or read book Network Data Mining And Analysis written by Ming Gao and published by World Scientific. This book was released on 2018-09-28 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: Online social networking sites like Facebook, LinkedIn, and Twitter, offer millions of members the opportunity to befriend one another, send messages to each other, and post content on the site — actions which generate mind-boggling amounts of data every day.To make sense of the massive data from these sites, we resort to social media mining to answer questions like the following:

Social Network Data Analytics

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Publisher : Springer Science & Business Media
ISBN 13 : 1441984623
Total Pages : 508 pages
Book Rating : 4.4/5 (419 download)

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Book Synopsis Social Network Data Analytics by : Charu C. Aggarwal

Download or read book Social Network Data Analytics written by Charu C. Aggarwal and published by Springer Science & Business Media. This book was released on 2011-03-18 with total page 508 pages. Available in PDF, EPUB and Kindle. Book excerpt: Social network analysis applications have experienced tremendous advances within the last few years due in part to increasing trends towards users interacting with each other on the internet. Social networks are organized as graphs, and the data on social networks takes on the form of massive streams, which are mined for a variety of purposes. Social Network Data Analytics covers an important niche in the social network analytics field. This edited volume, contributed by prominent researchers in this field, presents a wide selection of topics on social network data mining such as Structural Properties of Social Networks, Algorithms for Structural Discovery of Social Networks and Content Analysis in Social Networks. This book is also unique in focussing on the data analytical aspects of social networks in the internet scenario, rather than the traditional sociology-driven emphasis prevalent in the existing books, which do not focus on the unique data-intensive characteristics of online social networks. Emphasis is placed on simplifying the content so that students and practitioners benefit from this book. This book targets advanced level students and researchers concentrating on computer science as a secondary text or reference book. Data mining, database, information security, electronic commerce and machine learning professionals will find this book a valuable asset, as well as primary associations such as ACM, IEEE and Management Science.

Graph Mining

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Publisher : Morgan & Claypool Publishers
ISBN 13 : 160845116X
Total Pages : 209 pages
Book Rating : 4.6/5 (84 download)

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Book Synopsis Graph Mining by : Deepayan Chakrabarti

Download or read book Graph Mining written by Deepayan Chakrabarti and published by Morgan & Claypool Publishers. This book was released on 2012-10-01 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: What does the Web look like? How can we find patterns, communities, outliers, in a social network? Which are the most central nodes in a network? These are the questions that motivate this work. Networks and graphs appear in many diverse settings, for example in social networks, computer-communication networks (intrusion detection, traffic management), protein-protein interaction networks in biology, document-text bipartite graphs in text retrieval, person-account graphs in financial fraud detection, and others. In this work, first we list several surprising patterns that real graphs tend to follow. Then we give a detailed list of generators that try to mirror these patterns. Generators are important, because they can help with "what if" scenarios, extrapolations, and anonymization. Then we provide a list of powerful tools for graph analysis, and specifically spectral methods (Singular Value Decomposition (SVD)), tensors, and case studies like the famous "pageRank" algorithm and the "HITS" algorithm for ranking web search results. Finally, we conclude with a survey of tools and observations from related fields like sociology, which provide complementary viewpoints. Table of Contents: Introduction / Patterns in Static Graphs / Patterns in Evolving Graphs / Patterns in Weighted Graphs / Discussion: The Structure of Specific Graphs / Discussion: Power Laws and Deviations / Summary of Patterns / Graph Generators / Preferential Attachment and Variants / Incorporating Geographical Information / The RMat / Graph Generation by Kronecker Multiplication / Summary and Practitioner's Guide / SVD, Random Walks, and Tensors / Tensors / Community Detection / Influence/Virus Propagation and Immunization / Case Studies / Social Networks / Other Related Work / Conclusions

Community Detection and Mining in Social Media

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Publisher : Morgan & Claypool Publishers
ISBN 13 : 1608453553
Total Pages : 137 pages
Book Rating : 4.6/5 (84 download)

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Book Synopsis Community Detection and Mining in Social Media by : Lei Tang

Download or read book Community Detection and Mining in Social Media written by Lei Tang and published by Morgan & Claypool Publishers. This book was released on 2010-05-05 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: The past decade has witnessed the emergence of participatory Web and social media, bringing people together in many creative ways. Millions of users are playing, tagging, working, and socializing online, demonstrating new forms of collaboration, communication, and intelligence that were hardly imaginable just a short time ago. Social media also helps reshape business models, sway opinions and emotions, and opens up numerous possibilities to study human interaction and collective behavior in an unparalleled scale. This lecture, from a data mining perspective, introduces characteristics of social media, reviews representative tasks of computing with social media, and illustrates associated challenges. It introduces basic concepts, presents state-of-the-art algorithms with easy-to-understand examples, and recommends effective evaluation methods. In particular, we discuss graph-based community detection techniques and many important extensions that handle dynamic, heterogeneous networks in social media. We also demonstrate how discovered patterns of communities can be used for social media mining. The concepts, algorithms, and methods presented in this lecture can help harness the power of social media and support building socially-intelligent systems. This book is an accessible introduction to the study of \emph{community detection and mining in social media}. It is an essential reading for students, researchers, and practitioners in disciplines and applications where social media is a key source of data that piques our curiosity to understand, manage, innovate, and excel. This book is supported by additional materials, including lecture slides, the complete set of figures, key references, some toy data sets used in the book, and the source code of representative algorithms. The readers are encouraged to visit the book website for the latest information. Table of Contents: Social Media and Social Computing / Nodes, Ties, and Influence / Community Detection and Evaluation / Communities in Heterogeneous Networks / Social Media Mining

Advanced Data Mining and Applications

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Publisher : Springer Nature
ISBN 13 : 3030653900
Total Pages : 673 pages
Book Rating : 4.0/5 (36 download)

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Book Synopsis Advanced Data Mining and Applications by : Xiaochun Yang

Download or read book Advanced Data Mining and Applications written by Xiaochun Yang and published by Springer Nature. This book was released on 2021-01-05 with total page 673 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 16th International Conference on Advanced Data Mining and Applications, ADMA 2020, held in Foshan, China in November 2020. The 35 full papers presented together with 14 short papers papers were carefully reviewed and selected from 96 submissions. The papers were organized in topical sections named: Machine Learning; Text Mining; Graph Mining; Predictive Analytics; Recommender Systems; Privacy and Security; Query Processing; Data Mining Applications.

Discovery Science

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Publisher : Springer Science & Business Media
ISBN 13 : 3642047467
Total Pages : 487 pages
Book Rating : 4.6/5 (42 download)

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Book Synopsis Discovery Science by : João Gama

Download or read book Discovery Science written by João Gama and published by Springer Science & Business Media. This book was released on 2009-10-05 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the twelfth International Conference, on Discovery Science, DS 2009, held in Porto, Portugal, in October 2009. The 35 revised full papers presented were carefully selected from 92 papers. The scope of the conference includes the development and analysis of methods for automatic scientific knowledge discovery, machine learning, intelligent data analysis, theory of learning, as well as their applications.

Social Media Mining

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Publisher : Cambridge University Press
ISBN 13 : 1107018854
Total Pages : 337 pages
Book Rating : 4.1/5 (7 download)

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Book Synopsis Social Media Mining by : Reza Zafarani

Download or read book Social Media Mining written by Reza Zafarani and published by Cambridge University Press. This book was released on 2014-04-28 with total page 337 pages. Available in PDF, EPUB and Kindle. Book excerpt: Integrates social media, social network analysis, and data mining to provide an understanding of the potentials of social media mining.

Data Mining: Concepts and Techniques

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Publisher : Elsevier
ISBN 13 : 0123814804
Total Pages : 740 pages
Book Rating : 4.1/5 (238 download)

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Book Synopsis Data Mining: Concepts and Techniques by : Jiawei Han

Download or read book Data Mining: Concepts and Techniques written by Jiawei Han and published by Elsevier. This book was released on 2011-06-09 with total page 740 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Mining: Concepts and Techniques provides the concepts and techniques in processing gathered data or information, which will be used in various applications. Specifically, it explains data mining and the tools used in discovering knowledge from the collected data. This book is referred as the knowledge discovery from data (KDD). It focuses on the feasibility, usefulness, effectiveness, and scalability of techniques of large data sets. After describing data mining, this edition explains the methods of knowing, preprocessing, processing, and warehousing data. It then presents information about data warehouses, online analytical processing (OLAP), and data cube technology. Then, the methods involved in mining frequent patterns, associations, and correlations for large data sets are described. The book details the methods for data classification and introduces the concepts and methods for data clustering. The remaining chapters discuss the outlier detection and the trends, applications, and research frontiers in data mining. This book is intended for Computer Science students, application developers, business professionals, and researchers who seek information on data mining. - Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects - Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields - Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of your data

Principles of Data Mining

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Publisher : MIT Press
ISBN 13 : 9780262082907
Total Pages : 594 pages
Book Rating : 4.0/5 (829 download)

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Book Synopsis Principles of Data Mining by : David J. Hand

Download or read book Principles of Data Mining written by David J. Hand and published by MIT Press. This book was released on 2001-08-17 with total page 594 pages. Available in PDF, EPUB and Kindle. Book excerpt: The first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The growing interest in data mining is motivated by a common problem across disciplines: how does one store, access, model, and ultimately describe and understand very large data sets? Historically, different aspects of data mining have been addressed independently by different disciplines. This is the first truly interdisciplinary text on data mining, blending the contributions of information science, computer science, and statistics. The book consists of three sections. The first, foundations, provides a tutorial overview of the principles underlying data mining algorithms and their application. The presentation emphasizes intuition rather than rigor. The second section, data mining algorithms, shows how algorithms are constructed to solve specific problems in a principled manner. The algorithms covered include trees and rules for classification and regression, association rules, belief networks, classical statistical models, nonlinear models such as neural networks, and local "memory-based" models. The third section shows how all of the preceding analysis fits together when applied to real-world data mining problems. Topics include the role of metadata, how to handle missing data, and data preprocessing.