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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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Time Series Clustering and Classification

clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes relevant;

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Time Series Clustering and Classification

clustering and classifying different types of large datasets, including time series data. Time Series Clustering and Classification includes;

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SAP Predictive Analytics The Comprehensive Guide Rheinwerk Publishing

Leave your crystal ball behind and peer into the future with SAPPredictive Analytics. Master predictive models-regression, time series;

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Lecture Notes In Data Mining

(classification, clustering and association rules) by offering overviews that include both analysis and insight.The initial chapters lay a framework;

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KNN Classifier and K-Means Clustering for Robust Classification of Epilepsy from EEG Signals. A Detailed Analysis

Knn Classifier and K-Means Clustering Fo;

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Mixture Model-Based Classification

This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a;

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Mixture Model-Based Classification

This is a great overview of the field of model-based clustering and classification by one of its leading developers. McNicholas provides a;

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New Frontiers in Mining Complex Patterns

were carefully reviewed and selected from numerous submissions. The papers are organized in topical sections on data streams and time series;

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Fusion Methods for Time-Series Classification

Time-series classification is the common theoretical background of many recognition tasks performed by computers, such as handwriting;

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Satellite Image Analysis Clustering and Classification

information. Highly accurate automatic classification and decision support systems can facilitate the efforts of data analysts, reduce human error;

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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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Model-Based Clustering and Classification for Data Science

and classification in terms of statistical models, thus yielding principled estimation, testing and prediction methods, and sound answers to;

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Clustering Methodology for Symbolic Data

ready for analysis. * Features very large contemporary datasets such as time series, interval-valued data and histogram-valued data * Considers;

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Statistical Learning and Modeling in Data Analysis

The contributions gathered in this book focus on modern methods for statistical learning and modeling in data analysis and present a series;

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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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Classification, (Big) Data Analysis and Statistical Learning

This edited book focuses on the latest developments in classification, statistical learning, data analysis and related areas of data;

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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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Advanced Data Analytics Using Python

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;

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Advanced Data Analytics Using Python

Understand advanced data analytics concepts such as time series and principal component analysis with ETL, supervised learning, and PySpark;

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Advances in Soft Computing

networks and opinion mining; fuzzy logic; time series analysis and forecasting; planning and scheduling; image processing and computer vision;

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Advances in Computational Intelligence

; social networks and opinion mining; fuzzy logic; time series analysis and forecasting; planning and scheduling; image processing and computer;

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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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Applied Multivariate Statistics with R

multivariate normal distributions; factor methods; linear regression; discrimination and classification; clustering; time series models; and additional;

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Go Machine Learning Projects

environment with Go libraries Use Gonum to perform regression and classification Explore time series models and decompose trends with Go libraries;

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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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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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