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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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Evolutionary Data Clustering

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

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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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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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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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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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Machine Intelligence: Recent Advances

Reflects current research in natural language processing, data mining, networks and network security, robotics, image processing and;

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Advances in Knowledge Discovery and Data Mining

in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected;

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Advances in Knowledge Discovery and Data Mining

in Knowledge Discovery and Data Mining, PAKDD 2017, held in Jeju, South Korea, in May 2017. The 129 full papers were carefully reviewed and selected;

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Model-Based Clustering and Classification for Data Science

Cluster analysis finds groups in data automatically. Most methods have been heuristic and leave open such central questions as: how many;

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An Introduction to Clustering with R

of statistical techniques for classifying multivariate data into groups or clusters based on their similar features. Clustering is nowadays widely used;

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