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Fuzzy C-means Clustering using Pattern Recognition

The objective of this book is to talk about the usage of Fuzzy Logic in Pattern Recognition. There are different fuzzy approaches to;

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Fuzzy Approach to Hierarchical Clustering

of fuzzy c-means algorithm.;

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Fuzzy Clustering Via Proportional Membership Model

), modelling a typological classification. In fuzzy clustering, the fuzzy c-means (FCM) method has become one of the most popular techniques. As a;

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Fuzzy Sets & their Application to Clustering & Training

, particularly in pattern recognition, data mining, adaptive and hierarchical clustering, and classifier design. Fuzzy Sets and their Application to;

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Pattern Recognition

-simulation techniques; pattern recognition applied to spectroscopy; optimisation of an embedded simplified fuzzy ARTMAP implemented on a;

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Rough-Fuzzy Pattern Recognition

pattern recognition models. Based on the existing as well as new results, the book is structured according to the major phases of a pattern;

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Fuzzy Image Processing and Applications with MATLAB

detection, content-based image retrieval, pattern recognition, and clustering. They describe all components of fuzzy, detailing preprocessing;

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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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Advances In Fuzzy Clustering And Its Applications

, bioinformatics, data mining, image processing, and pattern recognition, while computer engineers, students and researchers, in most engineering;

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Apache Mahout Clustering Designs

of the algorithms, such as K-Means, Fuzzy K-Means, StreamingKMeans, and how to use Mahout to cluster your data using a particular algorithm. You;

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Computational Intelligence for Pattern Recognition

The book presents a comprehensive and up-to-date review of fuzzy pattern recognition. It carefully discusses a range of methodological and;

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Introduction To Pattern Recognition

structural approaches, novel topics such as fuzzy pattern recognition and pattern recognition via neural networks are also reviewed. Each topic;

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Unsupervised Machine Learning for Clustering in Political and Social Research

detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering;

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Fuzzy Information Processing

, including theory and applications of fuzzy numbers and sets, fuzzy logic, fuzzy inference systems, fuzzy clustering, fuzzy pattern classification;

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Mining Intelligence and Knowledge Exploration

. Accepted papers were grouped into various subtopics including information retrieval, machine learning, pattern recognition, knowledge discovery;

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Pattern Recognition Using Neural Networks

Pattern Regcognition with Neural Networks covers traditional linear pattern recognition and its nonlinear extension via neural networks;

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Machine Intelligence: Recent Advances

bioinformatics. Problems such as named entity recognition, creation of WordNet and part-of-speech tagging have been addressed in Section I. In Section II;

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Mining Intelligence and Knowledge Exploration

retrieval, machine learning, pattern recognition, knowledge discovery, classification, clustering, image processing, network security, speech;

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Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications

This book constitutes the refereed proceedings of the 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014, held in Puerto;

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Structural, Syntactic, and Statistical Pattern Recognition

This book constitutes the proceedings of the Joint IAPR International Workshop on Structural Syntactic, and Statistical Pattern Recognition;

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Time Series Clustering and Classification

Provides an overview of the methods and applications of pattern recognition of time series Covers a wide range of techniques, including;

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Time Series Clustering and Classification

. Features Provides an overview of the methods and applications of pattern recognition of time series Covers a wide;

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

, and their applications in areas such as: intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and;

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Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

, and their applications in areas such as: intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and;

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Handbook Of Pattern Recognition And Computer Vision

technology. There are eight new chapters on the latest developments in life sciences using pattern recognition as well as two new chapters on pattern;

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Data Clustering

algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology;

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Structural, Syntactic, and Statistical Pattern Recognition

from 81 submissions. The accepted papers cover the major topics of current interest in pattern recognition, including classification and;

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