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Adaptive Resonance Theory in Social Media Data Clustering

in social media domain Adaptive Resonance Theory in Social Media Data Clustering stands on the fundamental breakthrough in cognitive and neural;

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Graph-Based Social Media Analysis

, namely the confluence of graph analysis and network theory with linear algebra, digital media, machine learning, big data analysis, and signal;

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Fuzzy Sets & their Application to Clustering & Training

Fuzzy set theory - and its underlying fuzzy logic - represents one of the most significant scientific and cultural paradigms to emerge;

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Learning Social Media Analytics with R

Tap into the realm of social media and unleash the power of analytics for data-driven insights using R About This Book * A practical;

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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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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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Advanced Data Mining and Applications

social media; machine learning; classification and clustering methods; behavior modeling and user profiling; bioinformatics and medical data;

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Advanced Data Mining and Applications

; Recommender Systems; Social Network and Social Media; Behavior Modeling and User Profiling; Text and Multimedia Mining; Spatial-Temporal Data; Medical;

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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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Colorectal Cancer MRI Image Segmentation Using Image Processing Techniques

Master's Thesis from the year 2014 in the subject Medicine - Biomedical Engineering, grade: 76, cour;...

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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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Clustering in Bioinformatics and Drug Discovery

, exploratory data analysis, clustering algorithms, different types of data, graph theory, and various clustering forms. In the following chapters on;

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Clustering in Bioinformatics and Drug Discovery

, exploratory data analysis, clustering algorithms, different types of data, graph theory, and various clustering forms. In the following chapters on;

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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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Spectral Clustering And Biclustering

in data mining, multivariate statistics, or applied graph theory; but by skipping the proofs, the algorithms can also be used by specialists who;

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Big Data Analytics in Cognitive Social Media and Literary Texts

addresses the security and privacy of big data in social media, and the last focuses on the literary text as the literary data in Big Data Analytics;

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Mathematical Underpinnings of Analytics

meters; 'omic' data; sales promotions; social media; and microblogging. Each chapter tackles a topic in analytics: social networks and;

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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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Translation and Social Media: In Theory, in Training and in Professional Practice

Offering a discussion of translation and social media through three themes, theory, training and professional practice, this book builds on;

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Advances in Knowledge Discovery and Data Mining

selected from 405 submissions. They have been organized in topical sections named: social networks and social media; classification; machine;

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Advances in Knowledge Discovery and Data Mining

selected from 405 submissions. They have been organized in topical sections named: social networks and social media; classification; machine;

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Improved Scheduling Algorithm Using Dynamic Tree Construction for Wireless Sensor Networks

the Energy efficient clustering algorithm for data aggregation (EECA) are discussed. The LECSA (Load and Energy Consumption based Scheduling;

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Computational Intelligence in Pattern Recognition

neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining;

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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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Temporal Data Mining via Unsupervised Ensemble Learning

blend of theory and practice. Furthermore, the book includes illustrations of the proposed approaches based on data and simulation;

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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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