Algorithms For Clustering Data
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Algorithms For Clustering Data
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Author : Anil K. Jain
language : en
Publisher:
Release Date : 1988
Algorithms For Clustering Data written by Anil K. Jain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1988 with Computers categories.
Data Clustering
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Author : Guojun Gan
language : en
Publisher: SIAM
Release Date : 2007-07-12
Data Clustering written by Guojun Gan and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-07-12 with Mathematics categories.
Reference and compendium of algorithms for pattern recognition, data mining and statistical computing.
Data Clustering
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Author : Charu C. Aggarwal
language : en
Publisher: CRC Press
Release Date : 2016-03-29
Data Clustering written by Charu C. Aggarwal and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-03-29 with Business & Economics categories.
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.
Classification Clustering And Data Analysis
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Author : Krzystof Jajuga
language : en
Publisher: Springer Science & Business Media
Release Date : 2012-12-06
Classification Clustering And Data Analysis written by Krzystof Jajuga and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-12-06 with Computers categories.
The present volume contains a selection of papers presented at the Eighth Conference of the International Federation of Classification Societies (IFCS) which was held in Cracow, Poland, July 16-19, 2002. All originally submitted papers were subject to a reviewing process by two independent referees, a procedure which resulted in the selection of the 53 articles presented in this volume. These articles relate to theoretical investigations as well as to practical applications and cover a wide range of topics in the broad domain of classifi cation, data analysis and related methods. If we try to classify the wealth of problems, methods and approaches into some representative (partially over lapping) groups, we find in particular the following areas: • Clustering • Cluster validation • Discrimination • Multivariate data analysis • Statistical methods • Symbolic data analysis • Consensus trees and phylogeny • Regression trees • Neural networks and genetic algorithms • Applications in economics, medicine, biology, and psychology. Given the international orientation of IFCS conferences and the leading role of IFCS in the scientific world of classification, clustering and data anal ysis, this volume collects a representative selection of current research and modern applications in this field and serves as an up-to-date information source for statisticians, data analysts, data mining specialists and computer scientists.
Data Clustering Theory Algorithms And Applications Second Edition
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Author : Guojun Gan
language : en
Publisher: SIAM
Release Date : 2020-11-10
Data Clustering Theory Algorithms And Applications Second Edition written by Guojun Gan and has been published by SIAM this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-10 with Mathematics categories.
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.
Innovations In Data Methodologies And Computational Algorithms For Medical Applications
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Author : Gangopadhyay, Aryya
language : en
Publisher: IGI Global
Release Date : 2012-03-31
Innovations In Data Methodologies And Computational Algorithms For Medical Applications written by Gangopadhyay, Aryya and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-03-31 with Medical categories.
Medicine has, until recently, been slow to adapt to information technologies and systems for many reasons, but the future lies therein.Innovations in Data Methodologies and Computational Algorithms for Medical Applications offers the most cutting-edge research in the field, offering insights into case studies and methodologies from around the world. The text details the latest developments and will serve as a vital resource to practitioners and academics alike in the burgeoning field of medical applications of technologies. As security and privacy improve, Electronic Health Records and informatics in the medical field are becoming ubiquitous, and staying abreast of the latest information can be difficult. This volume serves as a reference handbook and theoretical framework for the future of the field.
Data Clustering In C
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Author : Guojun Gan
language : en
Publisher: CRC Press
Release Date : 2011-03-28
Data Clustering In C written by Guojun Gan and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-03-28 with Business & Economics categories.
Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that objects in the same group are similar and objects in different groups are quite distinct. Thousands of theoretical papers and a number of books on data clustering have been published over the past 50 years. However, few books exist to teach people how to implement data clustering algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set of reusable classes that simplifies the implementation of any data clustering algorithm. Readers can follow the development of the base data clustering classes and several popular data clustering algorithms. Additional topics such as data pre-processing, data visualization, cluster visualization, and cluster interpretation are briefly covered. This book is divided into three parts-- Data Clustering and C++ Preliminaries: A review of basic concepts of data clustering, the unified modeling language, object-oriented programming in C++, and design patterns A C++ Data Clustering Framework: The development of data clustering base classes Data Clustering Algorithms: The implementation of several popular data clustering algorithms A key to learning a clustering algorithm is to implement and experiment the clustering algorithm. Complete listings of classes, examples, unit test cases, and GNU configuration files are included in the appendices of this book as well as in the downloadable resources. The only requirements to compile the code are a modern C++ compiler and the Boost C++ libraries.
Modern Algorithms Of Cluster Analysis
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Author : Slawomir Wierzchoń
language : en
Publisher: Springer
Release Date : 2017-12-29
Modern Algorithms Of Cluster Analysis written by Slawomir Wierzchoń and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-12-29 with Technology & Engineering categories.
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.
Constrained Clustering
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Author : Sugato Basu
language : en
Publisher: CRC Press
Release Date : 2008-08-18
Constrained Clustering written by Sugato Basu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-08-18 with Business & Economics categories.
This volume encompasses many new types of constraints and clustering methods as well as delivers thorough coverage of the capabilities and limitations of constrained clustering. With contributions from industrial researchers and leading academic experts who pioneered the field, it provides a well-balanced combination of theoretical advances, key algorithmic development, and novel applications. The book presents various types of constraints for clustering and describes useful variations of the standard problem of clustering under constraints. It also demonstrates the application of clustering with constraints to relational, bibliographic, and video data.
Scalable Frameworks And Algorithms For Cluster Ensembles And Clustering Data Streams
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Author : Prodip Hore
language : en
Publisher:
Release Date : 2007
Scalable Frameworks And Algorithms For Cluster Ensembles And Clustering Data Streams written by Prodip Hore and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007 with categories.
ABSTRACT: Clustering algorithms are an important tool for data mining and data analysis purposes. Clustering algorithms fall under the category of unsupervised learning algorithms, which can group patterns without an external teacher or labels using some kind of similarity metric. Clustering algorithms are generally iterative in nature and computationally intensive. They will have disk accesses in every iteration for data sets larger than memory, making the algorithms unacceptably slow. Data could be processed in chunks, which fit into memory, to provide a scalable framework. Multiple processors may be used to process chunks in parallel. Clustering solutions from each chunk together form an ensemble and can be merged to provide a global solution. So, merging multiple clustering solutions, an ensemble, is important for providing a scalable framework. Combining multiple clustering solutions or partitions, is also important for obtaining a robust clustering solution, merging distributed clustering solutions, and providing a knowledge reuse and privacy preserving data mining framework. Here we address combining multiple clustering solutions in a scalable framework. We also propose algorithms for incrementally clustering large or very large data sets. We propose an algorithm that can cluster large data sets through a single pass. This algorithm is also extended to handle clustering infinite data streams. These types of incremental/online algorithms can be used for real time processing as they don't revisit data and are capable of processing data streams under the constraint of limited buffer size and computational time. Thus, different frameworks/algorithms have been proposed to address scalability issues in different settings. To our knowledge we are the first to introduce scalable algorithms for merging cluster ensembles, in terms of time and space complexity, on large real world data sets. We are also the first to introduce single pass and streaming variants of the fuzzy c means algorithm. We have evaluated the performance of our proposed frameworks/algorithms both on artificial and large real world data sets. A comparison of our algorithms with other relevant algorithms is discussed. These comparisons show the scalability and effectiveness of the partitions created by these new algorithms.