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: | Elektronisch E-Book |
| Sprache: | Englisch |
| Veröffentlicht: |
Cambridge
Cambridge University Press
2020
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| Schlagwörter: | |
| Links: | https://doi.org/10.1017/9781108883955 https://doi.org/10.1017/9781108883955 https://doi.org/10.1017/9781108883955 |
| 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: | Title from publisher's bibliographic system (viewed on 11 Jan 2021) |
| Umfang: | 1 Online-Ressource (60 Seiten) |
| ISBN: | 9781108883955 |
| DOI: | 10.1017/9781108883955 |
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| indexdate | 2024-12-20T19:11:45Z |
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| isbn | 9781108883955 |
| language | English |
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| spelling | Waggoner, Philip D. ca. 20./21. Jh. (DE-588)1228365040 aut Unsupervised machine learning for clustering in political and social research Philip D. Waggoner Cambridge Cambridge University Press 2020 1 Online-Ressource (60 Seiten) txt rdacontent c rdamedia cr rdacarrier Title from publisher's bibliographic system (viewed on 11 Jan 2021) 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 Social sciences / Research Political science / Research Cluster analysis / Computer programs Machine learning Erscheint auch als Druck-Ausgabe 978-1-108-79338-4 https://doi.org/10.1017/9781108883955 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Waggoner, Philip D. ca. 20./21. Jh Unsupervised machine learning for clustering in political and social research Social sciences / Research Political science / Research Cluster analysis / Computer programs Machine learning |
| 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 |
| topic | Social sciences / Research Political science / Research Cluster analysis / Computer programs Machine learning |
| topic_facet | Social sciences / Research Political science / Research Cluster analysis / Computer programs Machine learning |
| url | https://doi.org/10.1017/9781108883955 |
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