Unsupervised machine learning for clustering in political and social research:
In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers r...
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2020
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| Schriftenreihe: | Elements in Quantitative and Computational Methods for the Social Sciences
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| Zusammenfassung: | In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering |
| Beschreibung: | 1. Introduction; 2. Setting the stage for clustering; 3. Agglomerative hierarchical clustering; 4. k-means clustering; 5. Gaussian mixture models; 6. Advanced methods; 7. Conclusion |
| Umfang: | 60 Seiten Illustrationen, Diagramme |
| ISBN: | 9781108793384 |
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| 490 | 0 | |a Elements in Quantitative and Computational Methods for the Social Sciences | |
| 500 | |a 1. Introduction; 2. Setting the stage for clustering; 3. Agglomerative hierarchical clustering; 4. k-means clustering; 5. Gaussian mixture models; 6. Advanced methods; 7. Conclusion | ||
| 520 | |a In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering | ||
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe |z 978-1-108-88395-5 |
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| isbn | 9781108793384 |
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| series2 | Elements in Quantitative and Computational Methods for the Social Sciences |
| spelling | Waggoner, Philip D. ca. 20./21. Jh. Verfasser (DE-588)1228365040 aut Unsupervised machine learning for clustering in political and social research Philip D. Waggoner Cambridge Cambridge University Press 2020 60 Seiten Illustrationen, Diagramme txt rdacontent n rdamedia nc rdacarrier Elements in Quantitative and Computational Methods for the Social Sciences 1. Introduction; 2. Setting the stage for clustering; 3. Agglomerative hierarchical clustering; 4. k-means clustering; 5. Gaussian mixture models; 6. Advanced methods; 7. Conclusion In the age of data-driven problem-solving, applying sophisticated computational tools for explaining substantive phenomena is a valuable skill. Yet, application of methods assumes an understanding of the data, structure, and patterns that influence the broader research program. This Element offers researchers and teachers an introduction to clustering, which is a prominent class of unsupervised machine learning for exploring and understanding latent, non-random structure in data. A suite of widely used clustering techniques is covered in this Element, in addition to R code and real data to facilitate interaction with the concepts. Upon setting the stage for clustering, the following algorithms are detailed: agglomerative hierarchical clustering, k-means clustering, Gaussian mixture models, and at a higher-level, fuzzy C-means clustering, DBSCAN, and partitioning around medoids (k-medoids) clustering Erscheint auch als Online-Ausgabe 978-1-108-88395-5 |
| spellingShingle | Waggoner, Philip D. ca. 20./21. Jh Unsupervised machine learning for clustering in political and social research |
| title | Unsupervised machine learning for clustering in political and social research |
| title_auth | Unsupervised machine learning for clustering in political and social research |
| title_exact_search | Unsupervised machine learning for clustering in political and social research |
| title_full | Unsupervised machine learning for clustering in political and social research Philip D. Waggoner |
| title_fullStr | Unsupervised machine learning for clustering in political and social research Philip D. Waggoner |
| title_full_unstemmed | Unsupervised machine learning for clustering in political and social research Philip D. Waggoner |
| title_short | Unsupervised machine learning for clustering in political and social research |
| title_sort | unsupervised machine learning for clustering in political and social research |
| work_keys_str_mv | AT waggonerphilipd unsupervisedmachinelearningforclusteringinpoliticalandsocialresearch |