Search results for: link-mining-models-algorithms-and-applications

Link Mining Models Algorithms and Applications

Author : Philip S. Yu
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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.

Relational Data Clustering

Author : Bo Long
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A culmination of the authors’ years of extensive research on this topic, Relational Data Clustering: Models, Algorithms, and Applications addresses the fundamentals and applications of relational data clustering. It describes theoretic models and algorithms and, through examples, shows how to apply these models and algorithms to solve real-world problems. After defining the field, the book introduces different types of model formulations for relational data clustering, presents various algorithms for the corresponding models, and demonstrates applications of the models and algorithms through extensive experimental results. The authors cover six topics of relational data clustering: Clustering on bi-type heterogeneous relational data Multi-type heterogeneous relational data Homogeneous relational data clustering Clustering on the most general case of relational data Individual relational clustering framework Recent research on evolutionary clustering This book focuses on both practical algorithm derivation and theoretical framework construction for relational data clustering. It provides a complete, self-contained introduction to advances in the field.

Multikonferenz Wirtschaftsinformatik 2012

Author : Dirk Christian Mattfeld
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Learning Automata Approach for Social Networks

Author : Alireza Rezvanian
File Size : 78.54 MB
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This book begins by briefly explaining learning automata (LA) models and a recently developed cellular learning automaton (CLA) named wavefront CLA. Analyzing social networks is increasingly important, so as to identify behavioral patterns in interactions among individuals and in the networks’ evolution, and to develop the algorithms required for meaningful analysis. As an emerging artificial intelligence research area, learning automata (LA) has already had a significant impact in many areas of social networks. Here, the research areas related to learning and social networks are addressed from bibliometric and network analysis perspectives. In turn, the second part of the book highlights a range of LA-based applications addressing social network problems, from network sampling, community detection, link prediction, and trust management, to recommender systems and finally influence maximization. Given its scope, the book offers a valuable guide for all researchers whose work involves reinforcement learning, social networks and/or artificial intelligence.

Data Matching

Author : Peter Christen
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Data matching (also known as record or data linkage, entity resolution, object identification, or field matching) is the task of identifying, matching and merging records that correspond to the same entities from several databases or even within one database. Based on research in various domains including applied statistics, health informatics, data mining, machine learning, artificial intelligence, database management, and digital libraries, significant advances have been achieved over the last decade in all aspects of the data matching process, especially on how to improve the accuracy of data matching, and its scalability to large databases. Peter Christen’s book is divided into three parts: Part I, “Overview”, introduces the subject by presenting several sample applications and their special challenges, as well as a general overview of a generic data matching process. Part II, “Steps of the Data Matching Process”, then details its main steps like pre-processing, indexing, field and record comparison, classification, and quality evaluation. Lastly, part III, “Further Topics”, deals with specific aspects like privacy, real-time matching, or matching unstructured data. Finally, it briefly describes the main features of many research and open source systems available today. By providing the reader with a broad range of data matching concepts and techniques and touching on all aspects of the data matching process, this book helps researchers as well as students specializing in data quality or data matching aspects to familiarize themselves with recent research advances and to identify open research challenges in the area of data matching. To this end, each chapter of the book includes a final section that provides pointers to further background and research material. Practitioners will better understand the current state of the art in data matching as well as the internal workings and limitations of current systems. Especially, they will learn that it is often not feasible to simply implement an existing off-the-shelf data matching system without substantial adaption and customization. Such practical considerations are discussed for each of the major steps in the data matching process.

Data Mining Concepts Methodologies Tools and Applications

Author : Management Association, Information Resources
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Data mining continues to be an emerging interdisciplinary field that offers the ability to extract information from an existing data set and translate that knowledge for end-users into an understandable way. Data Mining: Concepts, Methodologies, Tools, and Applications is a comprehensive collection of research on the latest advancements and developments of data mining and how it fits into the current technological world.

Sociophysics An Introduction

Author : Parongama Sen
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This book discusses the study and analysis of the physical aspects of social systems and models, inspired by the analogy with familiar models of physical systems and possible applications of statistical physics tools. Unlike the traditional analysis of the physics of macroscopic many-body or condensed matter systems, which is now an established and mature subject, the upsurge in the physical analysis and modelling of social systems, which are clearly many-body dynamical systems, is a recent phenomenon. Though the major developments in sociophysics have taken place only recently, the earliest attempts of proposing "Social Physics" as a discipline are more than one and a half centuries old. Various developments in the mainstream physics of condensed matter systems have inspired and induced the recent growth of sociophysical analysis and models. In spite of the tremendous efforts of many scientists in recent years, the subject is still in its infancy and major challenges are yet to be taken up. An introduction to these challenges is the main motivation for this book.

Co Clustering

Author : Gérard Govaert
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Cluster or co-cluster analyses are important tools in a variety ofscientific areas. The introduction of this book presents a state ofthe art of already well-established, as well as more recent methodsof co-clustering. The authors mainly deal with the two-modepartitioning under different approaches, but pay particularattention to a probabilistic approach. Chapter 1 concerns clustering in general and the model-basedclustering in particular. The authors briefly review the classicalclustering methods and focus on the mixture model. They present anddiscuss the use of different mixtures adapted to different types ofdata. The algorithms used are described and related works withdifferent classical methods are presented and commented upon. Thischapter is useful in tackling the problem of co-clustering under the mixture approach. Chapter 2 is devoted tothe latent block model proposed in the mixture approach context.The authors discuss this model in detail and present its interestregarding co-clustering. Various algorithms are presented in ageneral context. Chapter 3 focuses on binary and categorical data.It presents, in detail, the appropriated latent block mixturemodels. Variants of these models and algorithms are presented andillustrated using examples. Chapter 4 focuses on contingency data.Mutual information, phi-squared and model-based co-clustering arestudied. Models, algorithms and connections among differentapproaches are described and illustrated. Chapter 5 presents thecase of continuous data. In the same way, the different approachesused in the previous chapters are extended to this situation. Contents 1. Cluster Analysis. 2. Model-Based Co-Clustering. 3. Co-Clustering of Binary and Categorical Data. 4. Co-Clustering of Contingency Tables. 5. Co-Clustering of Continuous Data. About the Authors Gérard Govaert is Professor at the University of Technologyof Compiègne, France. He is also a member of the CNRSLaboratory Heudiasyc (Heuristic and diagnostic of complex systems).His research interests include latent structure modeling, modelselection, model-based cluster analysis, block clustering andstatistical pattern recognition. He is one of the authors of theMIXMOD (MIXtureMODelling) software. Mohamed Nadif is Professor at the University of Paris-Descartes,France, where he is a member of LIPADE (Paris Descartes computerscience laboratory) in the Mathematics and Computer Sciencedepartment. His research interests include machine learning, datamining, model-based cluster analysis, co-clustering, factorizationand data analysis. Cluster Analysis is an important tool in a variety of scientificareas. Chapter 1 briefly presents a state of the art of alreadywell-established as well more recent methods. The hierarchical,partitioning and fuzzy approaches will be discussed amongst others.The authors review the difficulty of these classical methods intackling the high dimensionality, sparsity and scalability. Chapter2 discusses the interests of coclustering, presenting differentapproaches and defining a co-cluster. The authors focus onco-clustering as a simultaneous clustering and discuss the cases ofbinary, continuous and co-occurrence data. The criteria andalgorithms are described and illustrated on simulated and realdata. Chapter 3 considers co-clustering as a model-basedco-clustering. A latent block model is defined for different kindsof data. The estimation of parameters and co-clustering is tackledunder two approaches: maximum likelihood and classification maximumlikelihood. Hard and soft algorithms are described and applied onsimulated and real data. Chapter 4 considers co-clustering as amatrix approximation. The trifactorization approach is consideredand algorithms based on update rules are described. Links withnumerical and probabilistic approaches are established. Acombination of algorithms are proposed and evaluated on simulatedand real data. Chapter 5 considers a co-clustering or bi-clusteringas the search for coherent co-clusters in biological terms or theextraction of co-clusters under conditions. Classical algorithmswill be described and evaluated on simulated and real data.Different indices to evaluate the quality of coclusters are notedand used in numerical experiments.

Data Mining Principles Process Model and Applications

Author : Mahendra Tiwari
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Book provides sound knowledge of data mining principles, algorithms, machine learning, data mining process models, applications, and experiments done on open source tool WEKA.

Markov Chains Models Algorithms and Applications

Author : Wai-Ki Ching
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Talks about Markov chain models for modeling queueing sequences, Internet, re-manufacturing systems, reverse logistics, inventory systems, bio-informatics, DNA sequences, genetic networks, data mining, and other practical systems. This monograph serves as a tool for analyzing a variety of stochastic (probabilistic) systems.

Data Mining

Author : Mehmed Kantardzic
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Presents the latest techniques for analyzing and extracting information from large amounts of data in high-dimensional data spaces The revised and updated third edition of Data Mining contains in one volume an introduction to a systematic approach to the analysis of large data sets that integrates results from disciplines such as statistics, artificial intelligence, data bases, pattern recognition, and computer visualization. Advances in deep learning technology have opened an entire new spectrum of applications. The author—a noted expert on the topic—explains the basic concepts, models, and methodologies that have been developed in recent years. This new edition introduces and expands on many topics, as well as providing revised sections on software tools and data mining applications. Additional changes include an updated list of references for further study, and an extended list of problems and questions that relate to each chapter.This third edition presents new and expanded information that: • Explores big data and cloud computing • Examines deep learning • Includes information on convolutional neural networks (CNN) • Offers reinforcement learning • Contains semi-supervised learning and S3VM • Reviews model evaluation for unbalanced data Written for graduate students in computer science, computer engineers, and computer information systems professionals, the updated third edition of Data Mining continues to provide an essential guide to the basic principles of the technology and the most recent developments in the field.

Advanced Data Mining and Applications

Author : Changjie Tang
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The Fourth International Conference on Advanced Data Mining and Applications (ADMA 2008) will be held in Chengdu, China, followed by the last three successful ADMA conferences (2005 in Wu Han, 2006 in Xi'an, and 2007 Harbin). Our major goal of ADMA is to bring together the experts on data mining in the world, and to provide a leading international forum for the dissemination of original research results in data mining, including applications, algorithms, software and systems, and different disciplines with potential applications of data mining. This goal has been partially achieved in a very short time despite the young age of the conference, thanks to the rigorous review process insisted upon, the outstanding list of internationally renowned keynote speakers and the excellent program each year. ADMA is ranked higher than, or very similar to, other data mining conferences (such as PAKDD, PKDD, and SDM) in early 2008 by an independent source: cs-conference-ranking. org. This year we had the pleasure and honor to host illustrious keynote speakers. Our distinguished keynote speakers are Prof. Qiang Yang and Prof. Jiming Liu. Prof. Yang is a tenured Professor and postgraduate studies coordinator at Computer Science and Engineering Department of Hong Kong University of Science and Technology. He is also a member of AAAI, ACM, a senior member of the IEEE, and he is also an as- ciate editor for the IEEE TKDE and IEEE Intelligent Systems, KAIS and WI Journals.

Data Mining Foundations and Intelligent Paradigms

Author : Dawn E. Holmes
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There are many invaluable books available on data mining theory and applications. However, in compiling a volume titled “DATA MINING: Foundations and Intelligent Paradigms: Volume 2: Core Topics including Statistical, Time-Series and Bayesian Analysis” we wish to introduce some of the latest developments to a broad audience of both specialists and non-specialists in this field.

Association Rule Mining

Author : Zhang (Shichao.)
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Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention. The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.

Data Clustering

Author : Charu C. Aggarwal
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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.

Managing and Mining Uncertain Data

Author : Charu C. Aggarwal
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Managing and Mining Uncertain Data, a survey with chapters by a variety of well known researchers in the data mining field, presents the most recent models, algorithms, and applications in the uncertain data mining field in a structured and concise way. This book is organized to make it more accessible to applications-driven practitioners for solving real problems. Also, given the lack of structurally organized information on this topic, Managing and Mining Uncertain Data provides insights which are not easily accessible elsewhere. Managing and Mining Uncertain Data is designed for a professional audience composed of researchers and practitioners in industry. This book is also suitable as a reference book for advanced-level students in computer science and engineering, as well as the ACM, IEEE, SIAM, INFORMS and AAAI Society groups.

Advanced Data Mining and Applications

Author : Reda Alhajj
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This book constitutes the refereed proceedings of the Third International Conference on Advanced Data Mining and Applications, ADMA 2007, held in Harbin, China in August 2007. The papers focus on advancements in data mining and peculiarities and challenges of real world applications using data mining.

Practical Applications of Data Mining

Author : Sang C. Suh
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Various topics of data mining techniques are identified and described throughout, including clustering, association rules, rough set theory, probability theory, neural networks, classification, and fuzzy logic. Each of these techniques is explored with a theoretical introduction and its effectiveness is demonstrated with various chapter examples.

Handbook of Research on Text and Web Mining Technologies

Author : Song, Min
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Examines recent advances and surveys of applications in text and web mining which should be of interest to researchers and end-users alike.

Text Clustering Models Algorithms and Applications

Author : Zuobing Xu
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