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Data Clustering in C++

and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set;

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Data Clustering in C++

and programming techniques, Data Clustering in C++ exploits the commonalities of all data clustering algorithms to create a flexible set;

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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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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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Fuzzy Clustering Via Proportional Membership Model

to the statistical paradigm at which the data are considered generated by a cluster structure, while crisp c-means does. The present work;

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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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Data Clustering

on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50;

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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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Machine Intelligence: Recent Advances

spatial data clustering, besides work on forecasting technique and seed selection for C-means and k-means clustering algorithms. Similarly;

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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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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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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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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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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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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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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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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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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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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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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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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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Secure Clustering in Multi-Modal Wireless Sensor Networks

based clustering technique that takes node's residual energy, density and number of features sensed in Multi-Modal WSNs. We propose a novel data;

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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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