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Classifying and Clustering Data with R [Video]

Dr. Bharatendra Rai
Wednesday, August 23, 2017

An all inclusive guide to get well versed with Classifying and Clustering Data with R

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Video Details

ISBN 139781788294904

Course Length2 hours 29 minutes

Video Description

This video course provides the steps you need to carry out classification and clustering with R/RStudio software. You’ll understand hierarchical clustering, non-hierarchical clustering, density-based clustering, and clustering of tweets. It also provides steps to carry out classification using discriminant analysis and decision tree methods.

In addition, we cover time-series decomposition, forecasting, clustering, and classification. It includes several example sets of data that you can use for the methodologies covered. The approaches are illustrated using practical applications to data belonging to various fields.

By the end the course, you will be well-versed with clustering and classification using Cluster Analysis, Discriminant Analysis, Time-series Analysis, and decision trees.

Style and Approach

In depth content balanced with tutorials that put theory into practice. The Video course is a Practical tutorials to help you get beyond the basics of data analysis with R, using real-world data sets and examples.

Cluster Analysis

The Course Overview

Iris Data

Hierarchical Clustering Using Dendrogram with R

Nonhierarchical K-means Clustering with R

Preparing Data and Packages for Density-based Clustering

Density-based Clustering with R

Text Data Preparation for Clustering

Clustering Words or Tweets with R

Discriminant Analysis

Discriminant Analysis with R

Model Interpretation

Visualization

Model Assessment

Time Series Analysis

Time Series Decomposition with R

Time Series Forecasting with R

Time Series Clustering with R

Time Series Classification with R

Decision Tree

Decision Tree with R

Visualization of Decision Trees

Prediction and Misclassification Errors

What You Will Learn

Know how to use hierarchical cluster analysis
Carry out cluster analysis using visualization methods such as Dendrogram and Silhouette plots
Find out how to perform non-hierarchical k-means clustering
See how to perform density-based clustering and clustering of tweets
Apply discriminant analysis for classification problems
Understand time-series decomposition, forecasting, clustering, and classification
Develop decision tree model for classification and prediction

Authors

Dr. Bharatendra Rai

Dr. Bharatendra Rai is Professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne State University, Detroit. His two master's degrees include specializations in quality, reliability, and OR from Indian Statistical Institute and another in statistics from Meerut University, India. He teaches courses on topics such as Analyzing Big Data, Business Analytics and Data Mining, Twitter and Text Analytics, Applied Decision Techniques, Operations Management, and Data Science for Business. He has over twenty years' consulting and training experience, including industries such as automotive, cutting tool, electronics, food, software, chemical, defense, and so on, in the areas of SPC, design of experiments, quality engineering, problem solving tools, Six-Sigma, and QMS. His work experience includes extensive research experience over five years at Ford in the areas of quality, reliability, and six-sigma. His research publications include journals such as IEEE Transactions on Reliability, Reliability Engineering & System Safety, Quality Engineering, International Journal of Product Development, International Journal of Business Excellence, and JSSSE. He has been keynote speaker at conferences and presented his research work at conferences such as SAE World Conference, INFORMS Annual Meetings, Industrial Engineering Research Conference, ASQs Annual Quality Congress, Taguchi's Robust Engineering Symposium, and Canadian RAMS.

Dr. Rai has won awards for Excellence and exemplary teamwork at Ford for his contributions in the area of applied statistics. He also received an Employee Recognition Award by FAIA for his Ph.D. dissertation in support of Ford Motor Company. He is certified as ISO 9000 lead assessor from British Standards Institute, ISO 14000 lead assessor from Marsden Environmental International, and Six Sigma Black Belt from ASQ.