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 :
Packt Publishing Ltd,
2019.
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| 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. |
| Beschreibung: | Exercise 19: Studying the Effect of Changing Kernels on a Distribution |
| Beschreibung: | 1 online resource (320 pages) |
| ISBN: | 1789951461 9781789951462 |
Internformat
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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. | ||
| 650 | 0 | |a R (Computer program language) |0 http://id.loc.gov/authorities/subjects/sh2002004407 | |
| 650 | 0 | |a Cluster analysis |x Computer programs. |0 http://id.loc.gov/authorities/subjects/sh85027251 | |
| 650 | 0 | |a Probabilities |x Data processing. | |
| 650 | 0 | |a Algorithms. |0 http://id.loc.gov/authorities/subjects/sh85003487 | |
| 650 | 2 | |a Algorithms |0 https://id.nlm.nih.gov/mesh/D000465 | |
| 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 | |
| 650 | 7 | |a Cluster analysis |x Computer programs |2 fast | |
| 650 | 7 | |a Probabilities |x Data processing |2 fast | |
| 650 | 7 | |a R (Computer program language) |2 fast | |
| 700 | 1 | |a Tuckfield, Bradford. | |
| 758 | |i has work: |a Applied unsupervised learning with R (Text) |1 https://id.oclc.org/worldcat/entity/E39PCYGhqr69TMKw3mBM8tRhYd |4 https://id.oclc.org/worldcat/ontology/hasWork | ||
| 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 | |l FWS01 |p ZDB-4-EBA |q FWS_PDA_EBA |u https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2092865 |3 Volltext |
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Datensatz im Suchindex
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| _version_ | 1816882489432473600 |
| adam_text | |
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| author | Malik, Alok |
| author2 | Tuckfield, Bradford |
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| author_facet | Malik, Alok Tuckfield, Bradford |
| author_role | |
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| building | Verbundindex |
| bvnumber | localFWS |
| callnumber-first | Q - Science |
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| callnumber-raw | QA276.45.R3 |
| callnumber-search | QA276.45.R3 |
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| callnumber-subject | QA - Mathematics |
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| contents | 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 |
| ctrlnum | (OCoLC)1096227855 |
| dewey-full | 519.502855133 |
| dewey-hundreds | 500 - Natural sciences and mathematics |
| dewey-ones | 519 - Probabilities and applied mathematics |
| dewey-raw | 519.502855133 |
| dewey-search | 519.502855133 |
| dewey-sort | 3519.502855133 |
| dewey-tens | 510 - Mathematics |
| discipline | Mathematik |
| format | Electronic eBook |
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| id | ZDB-4-EBA-on1096227855 |
| illustrated | Not Illustrated |
| indexdate | 2024-11-27T13:29:25Z |
| institution | BVB |
| isbn | 1789951461 9781789951462 |
| language | English |
| oclc_num | 1096227855 |
| open_access_boolean | |
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| owner_facet | MAIN DE-863 DE-BY-FWS |
| physical | 1 online resource (320 pages) |
| psigel | ZDB-4-EBA |
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| publishDateSearch | 2019 |
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| publisher | Packt Publishing Ltd, |
| record_format | marc |
| spelling | Malik, Alok. Applied Unsupervised Learning with R : Uncover Hidden Relationships and Patterns with K-Means Clustering, Hierarchical Clustering, and PCA. Birmingham : Packt Publishing Ltd, 2019. 1 online resource (320 pages) text txt rdacontent computer c rdamedia online resource cr rdacarrier Print version record. 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 Exercise 19: Studying the Effect of Changing Kernels on a Distribution 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. R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Cluster analysis Computer programs. http://id.loc.gov/authorities/subjects/sh85027251 Probabilities Data processing. Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 R (Langage de programmation) Probabilités Informatique. Algorithmes. algorithms. aat Data capture & analysis. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc Computers Data Processing. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Algorithms fast Cluster analysis Computer programs fast Probabilities Data processing fast R (Computer program language) fast Tuckfield, Bradford. has work: Applied unsupervised learning with R (Text) https://id.oclc.org/worldcat/entity/E39PCYGhqr69TMKw3mBM8tRhYd https://id.oclc.org/worldcat/ontology/hasWork Print version: Malik, Alok. Applied Unsupervised Learning with R : Uncover Hidden Relationships and Patterns with K-Means Clustering, Hierarchical Clustering, and PCA. Birmingham : Packt Publishing Ltd, ©2019 9781789956399 FWS01 ZDB-4-EBA FWS_PDA_EBA https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2092865 Volltext |
| spellingShingle | Malik, Alok Applied Unsupervised Learning with R : Uncover Hidden Relationships and Patterns with K-Means Clustering, Hierarchical Clustering, and PCA. 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 R (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Cluster analysis Computer programs. http://id.loc.gov/authorities/subjects/sh85027251 Probabilities Data processing. Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 R (Langage de programmation) Probabilités Informatique. Algorithmes. algorithms. aat Data capture & analysis. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc Computers Data Processing. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Algorithms fast Cluster analysis Computer programs fast Probabilities Data processing fast R (Computer program language) fast |
| subject_GND | http://id.loc.gov/authorities/subjects/sh2002004407 http://id.loc.gov/authorities/subjects/sh85027251 http://id.loc.gov/authorities/subjects/sh85003487 https://id.nlm.nih.gov/mesh/D000465 |
| 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_full | Applied Unsupervised Learning with R : Uncover Hidden Relationships and Patterns with K-Means Clustering, Hierarchical Clustering, and PCA. |
| title_fullStr | Applied Unsupervised Learning with R : Uncover Hidden Relationships and Patterns with K-Means Clustering, Hierarchical Clustering, and PCA. |
| title_full_unstemmed | Applied Unsupervised Learning with R : Uncover Hidden Relationships and Patterns with K-Means Clustering, Hierarchical Clustering, and PCA. |
| 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 (Computer program language) http://id.loc.gov/authorities/subjects/sh2002004407 Cluster analysis Computer programs. http://id.loc.gov/authorities/subjects/sh85027251 Probabilities Data processing. Algorithms. http://id.loc.gov/authorities/subjects/sh85003487 Algorithms https://id.nlm.nih.gov/mesh/D000465 R (Langage de programmation) Probabilités Informatique. Algorithmes. algorithms. aat Data capture & analysis. bicssc Neural networks & fuzzy systems. bicssc Artificial intelligence. bicssc Computers Data Processing. bisacsh Computers Neural Networks. bisacsh Computers Intelligence (AI) & Semantics. bisacsh Algorithms fast Cluster analysis Computer programs fast Probabilities Data processing fast R (Computer program language) fast |
| topic_facet | R (Computer program language) Cluster analysis Computer programs. Probabilities Data processing. Algorithms. Algorithms R (Langage de programmation) Probabilités Informatique. Algorithmes. algorithms. Data capture & analysis. Neural networks & fuzzy systems. Artificial intelligence. Computers Data Processing. Computers Neural Networks. Computers Intelligence (AI) & Semantics. Cluster analysis Computer programs Probabilities Data processing |
| url | https://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&AN=2092865 |
| work_keys_str_mv | AT malikalok appliedunsupervisedlearningwithruncoverhiddenrelationshipsandpatternswithkmeansclusteringhierarchicalclusteringandpca AT tuckfieldbradford appliedunsupervisedlearningwithruncoverhiddenrelationshipsandpatternswithkmeansclusteringhierarchicalclusteringandpca |