Results 231 to 240 of about 938,196 (259)
AI-powered detection and quantification of post-harvest physiological deterioration (PPD) in cassava using YOLO foundation models and K-means clustering. [PDF]
Plant MethodsAyalde DG +7 more
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Symptom phenotyping in people with cystic fibrosis during acute pulmonary exacerbations using machine-learning K-means clustering analysis. [PDF]
J Cyst FibrosGill ER +6 more
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Identifying high-risk Fontan phenotypes using K-means clustering of cardiac magnetic resonance-based dyssynchrony metrics. [PDF]
J Cardiovasc Magn ResonGearhart A +8 more
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Sparse kernel <i>k</i>-means clustering. [PDF]
J Appl StatPark B, Park C, Hong S, Choi H.
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2023
In this chapter, we explore the K-means clustering algorithm, emphasizing an accessible approach by minimizing abstract mathematical theories. We present a concrete numerical example with a small dataset to illustrate how clusters can be formed using the K.means clustering algorithm.
Zhiyuan Wang +3 more
openaire +2 more sources
In this chapter, we explore the K-means clustering algorithm, emphasizing an accessible approach by minimizing abstract mathematical theories. We present a concrete numerical example with a small dataset to illustrate how clusters can be formed using the K.means clustering algorithm.
Zhiyuan Wang +3 more
openaire +2 more sources
Behavior Research Methods, 2013
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic,
Timmerman, Marieke E. +3 more
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To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic,
Timmerman, Marieke E. +3 more
openaire +2 more sources
2018
As we learned in Chaps. 7, 8, and 9, classification could help us make predictions on new observations. However, classification requires (human supervised) predefined label classes. What if we are in the early phases of a study and/or don’t have the required resources to manually define, derive, or generate these class labels?
Annalyn Ng, Kenneth Soo
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As we learned in Chaps. 7, 8, and 9, classification could help us make predictions on new observations. However, classification requires (human supervised) predefined label classes. What if we are in the early phases of a study and/or don’t have the required resources to manually define, derive, or generate these class labels?
Annalyn Ng, Kenneth Soo
openaire +2 more sources
2020
Please download the sample Excel files from https://github.com/hhohho/Learn-Data-Mining-through-Excel. Double-click Chapter3-1a.xlsx to open it.
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Please download the sample Excel files from https://github.com/hhohho/Learn-Data-Mining-through-Excel. Double-click Chapter3-1a.xlsx to open it.
openaire +1 more source