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Data Clustering

Data Clustering PDF Author: Guojun Gan
Publisher: SIAM
ISBN: 0898716233
Category : Mathematics
Languages : en
Pages : 466

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Book Description
Reference and compendium of algorithms for pattern recognition, data mining and statistical computing.

Data Clustering

Data Clustering PDF Author: Guojun Gan
Publisher: SIAM
ISBN: 0898716233
Category : Mathematics
Languages : en
Pages : 466

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Book Description
Reference and compendium of algorithms for pattern recognition, data mining and statistical computing.

Data Clustering

Data Clustering PDF Author: Charu C. Aggarwal
Publisher: CRC Press
ISBN: 1315360411
Category : Business & Economics
Languages : en
Pages : 652

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Book Description
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.

Data Clustering in C++

Data Clustering in C++ PDF Author: Guojun Gan
Publisher: CRC Press
ISBN: 1439862249
Category : Business & Economics
Languages : en
Pages : 520

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Book Description
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,

Recent Advances in Hybrid Metaheuristics for Data Clustering

Recent Advances in Hybrid Metaheuristics for Data Clustering PDF Author: Sourav De
Publisher: John Wiley & Sons
ISBN: 1119551595
Category : Computers
Languages : en
Pages : 200

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Book Description
An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics and their application to data clustering. Metaheuristics are designed to tackle complex clustering problems where classical clustering algorithms have failed to be either effective or efficient. The authors—noted experts on the topic—provide a text that can aid in the design and development of hybrid metaheuristics to be applied to data clustering. The book includes performance analysis of the hybrid metaheuristics in relationship to their conventional counterparts. In addition to providing a review of data clustering, the authors include in-depth analysis of different optimization algorithms. The text offers a step-by-step guide in the build-up of hybrid metaheuristics and to enhance comprehension. In addition, the book contains a range of real-life case studies and their applications. This important text: Includes performance analysis of the hybrid metaheuristics as related to their conventional counterparts Offers an in-depth analysis of a range of optimization algorithms Highlights a review of data clustering Contains a detailed overview of different standard metaheuristics in current use Presents a step-by-step guide to the build-up of hybrid metaheuristics Offers real-life case studies and applications Written for researchers, students and academics in computer science, mathematics, and engineering, Recent Advances in Hybrid Metaheuristics for Data Clustering provides a text that explores the current data clustering approaches using a range of computational intelligence techniques.

Clustering

Clustering PDF Author: Rui Xu
Publisher: John Wiley & Sons
ISBN: 0470382783
Category : Mathematics
Languages : en
Pages : 400

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Book Description
This is the first book to take a truly comprehensive look at clustering. It begins with an introduction to cluster analysis and goes on to explore: proximity measures; hierarchical clustering; partition clustering; neural network-based clustering; kernel-based clustering; sequential data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors assume no previous background in clustering and their generous inclusion of examples and references help make the subject matter comprehensible for readers of varying levels and backgrounds.

A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications

A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications PDF Author: Dmitri A. Viattchenin
Publisher: Springer
ISBN: 3642355366
Category : Technology & Engineering
Languages : en
Pages : 227

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Book Description
The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of objects. The proposed approach can be used for solving different classification problems. Here, some techniques that might be useful at this purpose are outlined, including a methodology for constructing a set of labeled objects for a semi-supervised clustering algorithm, a methodology for reducing analyzed attribute space dimensionality and a methods for asymmetric data processing. Moreover, a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani’s fuzzy inference systems is introduced. This book addresses engineers, scientists, professors, students and post-graduate students, who are interested in and work with fuzzy clustering and its applications

Clustering and Classification

Clustering and Classification PDF Author: P Arabie
Publisher: World Scientific
ISBN: 981450453X
Category : Computers
Languages : en
Pages : 500

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Book Description
At a moderately advanced level, this book seeks to cover the areas of clustering and related methods of data analysis where major advances are being made. Topics include: hierarchical clustering, variable selection and weighting, additive trees and other network models, relevance of neural network models to clustering, the role of computational complexity in cluster analysis, latent class approaches to cluster analysis, theory and method with applications of a hierarchical classes model in psychology and psychopathology, combinatorial data analysis, clusterwise aggregation of relations, review of the Japanese-language results on clustering, review of the Russian-language results on clustering and multidimensional scaling, practical advances, and significance tests. Contents:An Overview of Combinatorial Data Analysis (P Arabie & L J Hubert)Hierarchical Classification (A D Gordon)A Hierarchical Classes Model: Theory and Method with Applications in Psychology and Psychopathology (S Rosenberg et al.)Trees and Other Network Models for Representing Proximity Data (G De Soete & J D Carroll)Complexity Theory: An Introduction for Practitioners of Classification (W H E Day)Neural Networks for Clustering (F Murtagh)A Review of Cluster Analysis Research in Japan (A Okada)Clustering and Multidimensional Scaling in Russia (1960–1990): A Review (B G Mirkin & I Muchnik)Clustering Validation: Results and Implications for Applied Analyses (G W Milligan)Probability Models and Hypotheses Testing in Partitioning Cluster Analysis (H-H Bock) Readership: Advanced undergraduates and graduate students in mathematics, computer science and social science. keywords:Additive Trees;Alternating Least Squares;Clustering;Complexity;Evolutionary Trees;Flexible Manufacturing;Minimum Spanning Trees;Mixture Models;Multidimensional Scaling;Multimodality;Networks;Nonhierachical Classification;NP-Complete;Partitioning;Tree Structures;Two-Mode Clustering;Unltrametricity;Variable Selection and Weighting “… there is such a wealth of information … that even a beginner could learn a lot from it.” Chance

Metaheuristic Clustering

Metaheuristic Clustering PDF Author: Swagatam Das
Publisher: Springer Science & Business Media
ISBN: 3540921729
Category : Computers
Languages : en
Pages : 252

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Book Description
Cluster analysis means the organization of an unlabeled collection of objects or patterns into separate groups based on their similarity. The task of computerized data clustering has been approached from diverse domains of knowledge like graph theory, multivariate analysis, neural networks, fuzzy set theory, and so on. Clustering is often described as an unsupervised learning method but most of the traditional algorithms require a prior specification of the number of clusters in the data for guiding the partitioning process, thus making it not completely unsupervised. Modern data mining tools that predict future trends and behaviors for allowing businesses to make proactive and knowledge-driven decisions, demand fast and fully automatic clustering of very large datasets with minimal or no user intervention. In this volume, we formulate clustering as an optimization problem, where the best partitioning of a given dataset is achieved by minimizing/maximizing one (single-objective clustering) or more (multi-objective clustering) objective functions. Using several real world applications, we illustrate the performance of several metaheuristics, particularly the Differential Evolution algorithm when applied to both single and multi-objective clustering problems, where the number of clusters is not known beforehand and must be determined on the run. This volume comprises of 7 chapters including an introductory chapter giving the fundamental definitions and the last Chapter provides some important research challenges. Academics, scientists as well as engineers engaged in research, development and application of optimization techniques and data mining will find the comprehensive coverage of this book invaluable.

Intelligent Text Categorization and Clustering

Intelligent Text Categorization and Clustering PDF Author: Felipe M. G. França
Publisher: Springer Science & Business Media
ISBN: 3540856439
Category : Mathematics
Languages : en
Pages : 120

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Book Description
Automatic Text Categorization and Clustering are becoming more and more important as the amount of text in electronic format grows and the access to it becomes more necessary and widespread. Well known applications are spam filtering and web search, but a large number of everyday uses exist (intelligent web search, data mining, law enforcement, etc.) Currently, researchers are employing many intelligent techniques for text categorization and clustering, ranging from support vector machines and neural networks to Bayesian inference and algebraic methods, such as Latent Semantic Indexing. This volume offers a wide spectrum of research work developed for intelligent text categorization and clustering. In the following, we give a brief introduction of the chapters that are included in this book.

Intelligent Data Engineering and Automated Learning -- IDEAL 2012

Intelligent Data Engineering and Automated Learning -- IDEAL 2012 PDF Author: Hujun Yin
Publisher: Springer
ISBN: 3642326390
Category : Computers
Languages : en
Pages : 862

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Book Description
This book constitutes the refereed proceedings of the 13th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2012, held in Natal, Brazil, in August 2012. The 100 revised full papers presented were carefully reviewed and selected from more than 200 submissions for inclusion in the book and present the latest theoretical advances and real-world applications in computational intelligence.