2022
DOI: 10.3758/s13428-022-01795-7
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Simultaneous clustering and variable selection: A novel algorithm and model selection procedure

Abstract: The growing availability of high-dimensional data sets offers behavioral scientists an unprecedented opportunity to integrate the information hidden in the novel types of data (e.g., genetic data, social media data, and GPS tracks, etc.,) and thereby obtain a more detailed and comprehensive view towards their research questions. In the context of clustering, analyzing the large volume of variables could potentially result in an accurate estimation or a novel discovery of underlying subgroups. However, a unique… Show more

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Cited by 2 publications
(2 citation statements)
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References 48 publications
(87 reference statements)
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“…The methodology determining the optimal combination of K-means and DBSCAN can follow two methods. The first method involves searching all possible combinations of K and e sequentially to find the most effective configuration [67]. However, this method requires extensive resources and may be impractical during time-sensitive disasters.…”
Section: Results and Discussion A Analysis Of Clustering Resultsmentioning
confidence: 99%
“…The methodology determining the optimal combination of K-means and DBSCAN can follow two methods. The first method involves searching all possible combinations of K and e sequentially to find the most effective configuration [67]. However, this method requires extensive resources and may be impractical during time-sensitive disasters.…”
Section: Results and Discussion A Analysis Of Clustering Resultsmentioning
confidence: 99%
“…However, as the applications of ML algorithms expand in scope, and with the advent of wearable devices and other technological advances (Tufail et al, 2023), researchers confront two main challenges: (1) obtain a sufficiently large data set necessary for constructing meaningful machine learning models (L'Heureux et al, 2017) and (2) high-dimensional data sets are becoming more prevalent across multiple disciplines (Yuan, 2023) including genetics (Chi et al, 2016), organizational psychology, and neuroscience (Waldman et al, 2019). The effectiveness of machine learning models is inherently tied to the quality and quantity of data used for training.…”
Section: Introductionmentioning
confidence: 99%