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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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Metaheuristics for Big Data

critical challenges: infrastructure, for storage and transportation, and conceptual models. Finally, to extract meaning from Big Data requires;

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Hybrid Metaheuristics

illustrates the recent researches on evolving novel hybrid metaheuristic algorithms, and prominently highlights its diverse application areas. As such;

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Hybrid Metaheuristics

The main goal of this book is to provide a state of the art of hybrid metaheuristics. The book provides a complete background that enables;

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Hybrid Metaheuristics

This book constitutes the refereed proceedings of the 10th International Workshop on Hybrid Metaheuristics, HM 2016, held in Plymouth, UK;

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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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Hybrid Artificial Intelligent Systems

fusion, HAIS for computer security (HAISFCS), data mining: data preparation and analysis, hybrid artificial intelligence systems in management;

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Constrained Clustering

Since the initial work on constrained clustering, there have been numerous advances in methods, applications, and our understanding of the;

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Hybrid Metaheuristics

This book constitutes the refereed proceedings of the 9th International Workshop on Hybrid Metaheuristics, HM 2014, held in Hamburg;

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Data Clustering

of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social;

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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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Model-Based Clustering and Classification for Data Science

approaches to high-dimensional data and networks; and explains such recent advances as Bayesian regularization, non-Gaussian model-based clustering;

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Computational Intelligence in Pattern Recognition

in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the;

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Swarm Intelligence Based Optimization

20 submissions. They are centered around the following topics: theoretical advances of swarm intelligence metaheuristics; combinatorial;

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Metaheuristics for Portfolio Optimization

and strategic Portfolio Optimization models in Finance, that employ Metaheuristics for their effective solutions and demonstrates the results;

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Evolutionary Data Clustering

implementations for data clustering using classical and modern nature-inspired techniques. It features a range of proven and recent nature-inspired;

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Heuristics

Many advances have recently been made in metaheuristic methods, from theory to applications. The community of researchers claiming the;

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Satellite Image Analysis Clustering and Classification

Thanks to recent advances in sensors, communication and satellite technology, data storage, processing and networking capabilities;

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Metaheuristics

efficient metaheuristics for multi-objective optimization problems Designing hybrid, parallel, and distributed metaheuristics Implementing;

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Advances in Knowledge Discovery and Data Mining

This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances;

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Advances in Knowledge Discovery and Data Mining

This two-volume set, LNAI 10234 and 10235, constitutes the thoroughly refereed proceedings of the 21st Pacific-Asia Conference on Advances;

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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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Parallel Metaheuristics

can provide useful and practical solutions for a wide range of problems and application domains, with an emphasis on the fields;

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Advances in Network Clustering and Blockmodeling

networks analysis Advances in Network Clustering and Blockmodeling is an ideal book for graduate and undergraduate students taking courses on;

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Recent Advances in Biological Network Analysis

This book reviews recent advances in the emerging field of computational network biology with special emphasis on comparative network;

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