Applied unsupervised learning with R: uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Weitere Verfasser: | |
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham ; Mumbai
Packt
March 2019
|
| Schlagworte: | |
| Online-Zugang: | UBY01 UER01 |
| Zusammenfassung: | Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions Intro -- Preface -- Introduction to Clustering Methods -- Introduction -- Introduction to Clustering -- Uses of Clustering -- Introduction to the Iris Dataset -- Exercise 1: Exploring the Iris Dataset -- Types of Clustering -- Introduction to k-means Clustering -- Euclidean Distance -- Manhattan Distance -- Cosine Distance -- The Hamming Distance -- k-means Clustering Algorithm -- Steps to Implement k-means Clustering -- Exercise 2: Implementing k-means Clustering on the Iris Dataset -- Activity 1: k-means Clustering with Three Clusters -- Introduction to k-means Clustering with Built-In Functions -- k-means Clustering with Three Clusters -- Exercise 3: k-means Clustering with R Libraries -- Introduction to Market Segmentation -- Exercise 4: Exploring the Wholesale Customer Dataset -- Activity 2: Customer Segmentation with k-means -- Introduction to k-medoids Clustering -- The k-medoids Clustering Algorithm -- k-medoids Clustering Code -- Exercise 5: Implementing k-medoid Clustering -- k-means Clustering versus k-medoids Clustering -- Activity 3: Performing Customer Segmentation with k-medoids Clustering -- Deciding the Optimal Number of Clusters -- Types of Clustering Metrics -- Silhouette Score -- Exercise 6: Calculating the Silhouette Score -- Exercise 7: Identifying the Optimum Number of Clusters -- WSS/Elbow Method -- Exercise 8: Using WSS to Determine the Number of Clusters -- The Gap Statistic -- Exercise 9: Calculating the Ideal Number of Clusters with the Gap Statistic -- Activity 4: Finding the Ideal Number of Market Segments -- Summary -- Advanced Clustering Methods -- Introduction -- Introduction to k-modes Clustering -- Steps for k-Modes Clustering -- Exercise 10: Implementing k-modes Clustering -- Activity 5: Implementing k-modes Clustering on the Mushroom Dataset -- Introduction to Density-Based Clustering (DBSCAN) |
| Beschreibung: | 1 Online-Ressource (vi, 297 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781789951462 |
Internformat
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| 520 | 3 | |a Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions | |
| 520 | 3 | |a Intro -- Preface -- Introduction to Clustering Methods -- Introduction -- Introduction to Clustering -- Uses of Clustering -- Introduction to the Iris Dataset -- Exercise 1: Exploring the Iris Dataset -- Types of Clustering -- Introduction to k-means Clustering -- Euclidean Distance -- Manhattan Distance -- Cosine Distance -- The Hamming Distance -- k-means Clustering Algorithm -- Steps to Implement k-means Clustering -- Exercise 2: Implementing k-means Clustering on the Iris Dataset -- Activity 1: k-means Clustering with Three Clusters -- Introduction to k-means Clustering with Built-In Functions -- k-means Clustering with Three Clusters -- Exercise 3: k-means Clustering with R Libraries -- Introduction to Market Segmentation -- Exercise 4: Exploring the Wholesale Customer Dataset -- Activity 2: Customer Segmentation with k-means -- Introduction to k-medoids Clustering -- The k-medoids Clustering Algorithm -- k-medoids Clustering Code -- Exercise 5: Implementing k-medoid Clustering -- k-means Clustering versus k-medoids Clustering -- Activity 3: Performing Customer Segmentation with k-medoids Clustering -- Deciding the Optimal Number of Clusters -- Types of Clustering Metrics -- Silhouette Score -- Exercise 6: Calculating the Silhouette Score -- Exercise 7: Identifying the Optimum Number of Clusters -- WSS/Elbow Method -- Exercise 8: Using WSS to Determine the Number of Clusters -- The Gap Statistic -- Exercise 9: Calculating the Ideal Number of Clusters with the Gap Statistic -- Activity 4: Finding the Ideal Number of Market Segments -- Summary -- Advanced Clustering Methods -- Introduction -- Introduction to k-modes Clustering -- Steps for k-Modes Clustering -- Exercise 10: Implementing k-modes Clustering -- Activity 5: Implementing k-modes Clustering on the Mushroom Dataset -- Introduction to Density-Based Clustering (DBSCAN) | |
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| illustrated | Not Illustrated |
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| institution | BVB |
| isbn | 9781789951462 |
| language | English |
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| spelling | Malik, Alok Verfasser aut Applied unsupervised learning with R uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA Alok Malik and Bradford Tuckfield Birmingham ; Mumbai Packt March 2019 1 Online-Ressource (vi, 297 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Starting with the basics, Applied Unsupervised Learning with R explains clustering methods, distribution analysis, data encoders, and all features of R that enable you to understand your data better and get answers to all your business questions Intro -- Preface -- Introduction to Clustering Methods -- Introduction -- Introduction to Clustering -- Uses of Clustering -- Introduction to the Iris Dataset -- Exercise 1: Exploring the Iris Dataset -- Types of Clustering -- Introduction to k-means Clustering -- Euclidean Distance -- Manhattan Distance -- Cosine Distance -- The Hamming Distance -- k-means Clustering Algorithm -- Steps to Implement k-means Clustering -- Exercise 2: Implementing k-means Clustering on the Iris Dataset -- Activity 1: k-means Clustering with Three Clusters -- Introduction to k-means Clustering with Built-In Functions -- k-means Clustering with Three Clusters -- Exercise 3: k-means Clustering with R Libraries -- Introduction to Market Segmentation -- Exercise 4: Exploring the Wholesale Customer Dataset -- Activity 2: Customer Segmentation with k-means -- Introduction to k-medoids Clustering -- The k-medoids Clustering Algorithm -- k-medoids Clustering Code -- Exercise 5: Implementing k-medoid Clustering -- k-means Clustering versus k-medoids Clustering -- Activity 3: Performing Customer Segmentation with k-medoids Clustering -- Deciding the Optimal Number of Clusters -- Types of Clustering Metrics -- Silhouette Score -- Exercise 6: Calculating the Silhouette Score -- Exercise 7: Identifying the Optimum Number of Clusters -- WSS/Elbow Method -- Exercise 8: Using WSS to Determine the Number of Clusters -- The Gap Statistic -- Exercise 9: Calculating the Ideal Number of Clusters with the Gap Statistic -- Activity 4: Finding the Ideal Number of Market Segments -- Summary -- Advanced Clustering Methods -- Introduction -- Introduction to k-modes Clustering -- Steps for k-Modes Clustering -- Exercise 10: Implementing k-modes Clustering -- Activity 5: Implementing k-modes Clustering on the Mushroom Dataset -- Introduction to Density-Based Clustering (DBSCAN) R Programm (DE-588)4705956-4 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Data mining-Computer programs R (Computer program language) Electronic books Datenanalyse (DE-588)4123037-1 s R Programm (DE-588)4705956-4 s DE-604 Tuckfield, Bradford ctb Erscheint auch als Druck-Ausgabe 978-1-78995-146-2 |
| spellingShingle | Malik, Alok Applied unsupervised learning with R uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4705956-4 (DE-588)4123037-1 |
| title | Applied unsupervised learning with R uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA |
| title_auth | Applied unsupervised learning with R uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA |
| title_exact_search | Applied unsupervised learning with R uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA |
| title_exact_search_txtP | Applied unsupervised learning with R uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA |
| title_full | Applied unsupervised learning with R uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA Alok Malik and Bradford Tuckfield |
| title_fullStr | Applied unsupervised learning with R uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA Alok Malik and Bradford Tuckfield |
| title_full_unstemmed | Applied unsupervised learning with R uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA Alok Malik and Bradford Tuckfield |
| title_short | Applied unsupervised learning with R |
| title_sort | applied unsupervised learning with r uncover hidden relationships and patterns with k means clustering hierarchical clustering and pca |
| title_sub | uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA |
| topic | R Programm (DE-588)4705956-4 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | R Programm Datenanalyse |
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