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.
| Autor principal: | |
|---|---|
| Otros Autores: | |
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham :
Packt Publishing Ltd
2019.
|
| Colección: | EBSCO Academic eBook Collection Complete.
|
| Acceso en línea: | Conectar con la versión electrónica |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b45007858*spi |
Tabla de Contenidos:
- 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 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.
- 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.
- 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.
- 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.
