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Clustering Methodology for Symbolic Data

in order to extract knowledge from such a data. Classification Methodology for Symbolic Data: * Provides new classification methodologies for;

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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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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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Fuzzy Sets & their Application to Clustering & Training

clustering applications. It provides a unified mathematical framework for fuzzy classification and clustering, a methodology for developing training;

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Data Clustering in C++

algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design;

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Data Clustering in C++

algorithms. This book was written for anyone who wants to implement or improve their data clustering algorithms. Using object-oriented design;

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Advances in Data Science

produced today, and to improve prediction. Special kinds of data (symbolic, network, complex, compositional) are increasingly frequent in data;

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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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Evolutionary Data Clustering

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

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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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Fuzzy Approach to Hierarchical Clustering

for cluster analysis, to extend the definition of object dissimilarity for the case of fuzzy objects; to realize clustering of fuzzy data, to;

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Data Mining Methodology Development

Data Mining is a tool that extracts useful and novel patterns from great amounts of data. It usually involves 4 tasks: Classification;

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Recent Advances in Hybrid Metaheuristics for Data Clustering

intelligence techniques Recent Advances in Hybrid Metaheuristics for Data Clustering offers a guide to the fundamentals of various metaheuristics;

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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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Gene Expression Data Analysis

for generating gene expression data. Background knowledge on statistical modeling and machine learning techniques. Detailed methodology;

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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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Core Data Analysis: Summarization, Correlation, and Visualization

criteria for decision trees and data standardization, similarity and consensus clustering, modularity clustering and uniform partitioning. New;

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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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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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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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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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Data Analysis and Applications 1

, and SME modeling; and Part 3 presents symbolic data analysis, time series and multiple choice models, modeling in demography, and data mining.;

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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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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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