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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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Time Series Clustering and Classification

developments on observation-based, feature-based and model-based traditional and fuzzy clustering methods, feature-based and model-based classification;

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Time Series Clustering and Classification

The beginning of the age of artificial intelligence and machine learning has created new challenges and opportunities for data analysts;

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Mixture Model-Based Classification

focuses on the use of mixture model-based approaches for classification, with particular attention to clustering applications, and he has;

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Mixture Model-Based Classification

focuses on the use of mixture model-based approaches for classification, with particular attention to clustering applications, and he has;

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Fuzzy Clustering Via Proportional Membership Model

proposes a framework for typological classification based on a fuzzy clustering model of data generation.;

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Fundamentals of Clinical Data Science

excellence and value-based healthcare. Fundamentals of Clinical Data Science is an essential resource for healthcare professionals and IT consultants;

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Supervised and Unsupervised Learning for Data Science

of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models;

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Machine Learning Pocket Reference Working with Structured Data in Python

learning process and walks you through classification with structured data. You'll also learn methods for clustering, predicting a continuous value;

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Data Science for Marketing Analytics

understanding of regression techniques and tools for evaluating regression models, and explore ways to predict customer choice using classification;

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Data Science

. In particular, it includes contributions on classification methods for high-dimensional data, clustering methods, multivariate statistical methods, and;

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Lecture Notes In Data Mining

The continual explosion of information technology and the need for better data collection and management methods has made data mining an;

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Data Mining Algorithms

building classification, regression, and clustering models, as well as techniques used for attribute selection and transformation, model quality;

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Fundamentals of Data Analytics

data science is used for classification as well as supervised and unsupervised learning.;

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Fundamentals of Data Analytics

data science is used for classification as well as supervised and unsupervised learning.;

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Graph-theoretic Techniques For Web Content Mining

clustering and k-nearest neighbors classification, can be easily extended to work with graphs instead of vectors. This allows for the utilization;

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A Graph Theoretic Approach to Heterogeneous Data Clustering

other data types. We model this kind of data using a k-partite graph and partition it by considering it as a fusion of multiple bipartite graphs.;

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Advanced Analysis and Learning on Temporal Data

and embeddings for time series classification, clustering or for dimensionality reduction. The second part presents approaches on;

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Clustering Methodology for Symbolic Data

, Billard and Diday (2006) Classification Methodology for Symbolic Datais aimed at the practitioners of symbolic data analysis: statisticians and;

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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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Introduction to Multivariate Analysis

techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination;

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Introduction to Multivariate Analysis

techniques for clarifying phenomena behind observed multivariate data. It primarily focuses on regression modeling, classification and discrimination;

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Hands-On Data Science with Anaconda

to find solutions for clustering, classification, and linear regression -Analyze your data efficiently with the most powerful data science;

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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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Data Analysis and Rationality in a Complex World

developments and practical applications related to classification and clustering, data gathering, exploratory and multivariate data analysis;

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Data Science at the Command Line

Parallelize and distribute data-intensive pipelines Model data with dimensionality reduction, clustering, regression, and classification algorithms;

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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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