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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.
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Otros Autores: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd,
2019.
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| Temas: | |
| Acceso en línea: | Texto completo |
MARC
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| 100 | 1 | |a Malik, Alok. | |
| 245 | 1 | 0 | |a Applied Unsupervised Learning with R : |b Uncover Hidden Relationships and Patterns with K-Means Clustering, Hierarchical Clustering, and PCA. |
| 260 | |a Birmingham : |b Packt Publishing Ltd, |c 2019. | ||
| 300 | |a 1 online resource (320 pages) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 588 | 0 | |a Print version record. | |
| 505 | 0 | |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 | |
| 505 | 8 | |a K-means Clustering with Three ClustersExercise 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 | |
| 505 | 8 | |a Exercise 6: Calculating the Silhouette ScoreExercise 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 | |
| 505 | 8 | |a Introduction to Density-Based Clustering (DBSCAN)Steps for DBSCAN; Exercise 11: Implementing DBSCAN; Uses of DBSCAN; Activity 6: Implementing DBSCAN and Visualizing the Results; Introduction to Hierarchical Clustering; Types of Similarity Metrics; Steps to Perform Agglomerative Hierarchical Clustering; Exercise 12: Agglomerative Clustering with Different Similarity Measures; Divisive Clustering; Steps to Perform Divisive Clustering; Exercise 13: Performing DIANA Clustering; Activity 7: Performing Hierarchical Cluster Analysis on the Seeds Dataset; Summary; Probability Distributions | |
| 505 | 8 | |a IntroductionBasic Terminology of Probability Distributions; Uniform Distribution; Exercise 14: Generating and Plotting Uniform Samples in R; Normal Distribution; Exercise 15: Generating and Plotting a Normal Distribution in R; Skew and Kurtosis; Log-Normal Distributions; Exercise 16: Generating a Log-Normal Distribution from a Normal Distribution; The Binomial Distribution; Exercise 17: Generating a Binomial Distribution; The Poisson Distribution; The Pareto Distribution; Introduction to Kernel Density Estimation; KDE Algorithm; Exercise 18: Visualizing and Understanding KDE | |
| 500 | |a Exercise 19: Studying the Effect of Changing Kernels on a Distribution | ||
| 520 | |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. | ||
| 590 | |a eBooks on EBSCOhost |b EBSCO eBook Subscription Academic Collection - Worldwide | ||
| 650 | 0 | |a R (Computer program language) | |
| 650 | 0 | |a Cluster analysis |x Computer programs. | |
| 650 | 0 | |a Probabilities |x Data processing. | |
| 650 | 0 | |a Algorithms. | |
| 650 | 2 | |a Algorithms | |
| 650 | 6 | |a R (Langage de programmation) | |
| 650 | 6 | |a Probabilités |x Informatique. | |
| 650 | 6 | |a Algorithmes. | |
| 650 | 7 | |a algorithms. |2 aat | |
| 650 | 7 | |a Data capture & analysis. |2 bicssc | |
| 650 | 7 | |a Neural networks & fuzzy systems. |2 bicssc | |
| 650 | 7 | |a Artificial intelligence. |2 bicssc | |
| 650 | 7 | |a Computers |x Data Processing. |2 bisacsh | |
| 650 | 7 | |a Computers |x Neural Networks. |2 bisacsh | |
| 650 | 7 | |a Computers |x Intelligence (AI) & Semantics. |2 bisacsh | |
| 650 | 7 | |a Algorithms. |2 fast |0 (OCoLC)fst00805020 | |
| 650 | 7 | |a Cluster analysis |x Computer programs. |2 fast |0 (OCoLC)fst00864977 | |
| 650 | 7 | |a Probabilities |x Data processing. |2 fast |0 (OCoLC)fst01077741 | |
| 650 | 7 | |a R (Computer program language) |2 fast |0 (OCoLC)fst01086207 | |
| 700 | 1 | |a Tuckfield, Bradford. | |
| 776 | 0 | 8 | |i Print version: |a Malik, Alok. |t Applied Unsupervised Learning with R : Uncover Hidden Relationships and Patterns with K-Means Clustering, Hierarchical Clustering, and PCA. |d Birmingham : Packt Publishing Ltd, ©2019 |z 9781789956399 |
| 856 | 4 | 0 | |u https://ebsco.uam.elogim.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2092865 |z Texto completo |
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| 938 | |a ProQuest Ebook Central |b EBLB |n EBL5742601 | ||
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