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Evolutionary Data Clustering

for data clustering. The book provides literature reviews about single objective and multi-objective evolutionary clustering algorithms;

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Data Clustering

Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning;

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Data Clustering in C++

Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that;

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Data Clustering in C++

Data clustering is a highly interdisciplinary field, the goal of which is to divide a set of objects into homogeneous groups such that;

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Clustering

data clustering; large-scale data clustering; data visualization and high-dimensional data clustering; and cluster validation. The authors;

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A Graph Theoretic Approach to Heterogeneous Data Clustering

Data clustering is the process of automatically grouping data objects into different groups (clusters). The contribution of this book;

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Computational Intelligence in Pattern Recognition

This book presents practical development experiences in different areas of data analysis and pattern recognition, focusing on soft;

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Unsupervised Machine Learning for Clustering in Political and Social Research

In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable;

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Recent Advances in Hybrid Metaheuristics for Data Clustering

An authoritative guide to an in-depth analysis of various state-of-the-art data clustering approaches using a range of computational;

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Fuzzy Approach to Hierarchical Clustering

In this book we tried to extend the possibilities of hierarchical clustering methods to manipulate with fuzzy data both during preparing;

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Constrained Clustering

in Algorithms, Theory, and Applications presents an extensive collection of the latest innovations in clustering data analysis methods that use;

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Apache Mahout Clustering Designs

real world data sets Who This Book Is For This book is for developers who want to try out clustering on large datasets using Mahout. It;

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Intelligent Data Engineering and Automated Learning - IDEAL 2016

This book constitutes the refereed proceedings of the 17 International Conference on Intelligent Data Engineering and Automated Learning;

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Core Data Analysis: Summarization, Correlation, and Visualization

This text examines the goals of data analysis with respect to enhancing knowledge, and identifies data summarization and correlation;

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Intelligent Data Engineering and Automated Learning - IDEAL 2017

This book constitutes the refereed proceedings of the 18th International Conference on Intelligent Data Engineering and Automated Learning;

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Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition

global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and;

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Clustering

two above mentioned methods. Clustering: A Data Recovery Approach, Second Edition presents a unified modeling approach for the most;

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Clustering

two above mentioned methods. Clustering: A Data Recovery Approach, Second Edition presents a unified modeling approach for the most;

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Intelligent Data Engineering and Automated Learning -- IDEAL 2013

This book constitutes the refereed proceedings of the 14th International Conference on Intelligent Data Engineering and Automated Learning;

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Introduction to Clustering Large and High-Dimensional Data

There is a growing need for a more automated system of partitioning data sets into groups, or clusters. For example, digital libraries and;

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Temporal Data Mining via Unsupervised Ensemble Learning

Temporal Data Mining via Unsupervised Ensemble Learning provides the principle knowledge of temporal data mining in association with;

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Clustering in Bioinformatics and Drug Discovery

information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and;

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Clustering in Bioinformatics and Drug Discovery

information from pharmaceutical and biomedical data. It offers a concise overview of common and recent clustering methods used in bioinformatics and;

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Partitional Clustering via Nonsmooth Optimization

challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to;

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Partitional Clustering via Nonsmooth Optimization

challenges arising from big data. The book is ideal for anyone teaching or learning clustering algorithms. It provides an accessible introduction to;

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Computational Learning Approaches to Data Analytics in Biomedical Applications

Computational Learning Approaches to Data Analytics in Biomedical Applications provides a unified framework for biomedical data analysis;

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Metaheuristic Clustering Algorithm

usefulness as one of the steps in exploratory data analysis. Clustering is a challenging field of research in which its potential applications pose;

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