Understanding the K-Medians Problem

In this study, the general ideas surrounding the k-medians problem are discussed. This involves a look into what k-medians attempts to solve and how it goes about doing so. We take a look at why k-medians is used as opposed to its k-means counterpart, specifically how its robustness enables it to be...

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Published inProceedings of the International Conference on Scientific Computing (CSC) p. 219
Main Authors Whelan, Christopher, Harrell, Greg, Wang, Jin
Format Conference Proceeding
LanguageEnglish
Published Athens The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp) 01.01.2015
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Abstract In this study, the general ideas surrounding the k-medians problem are discussed. This involves a look into what k-medians attempts to solve and how it goes about doing so. We take a look at why k-medians is used as opposed to its k-means counterpart, specifically how its robustness enables it to be far more resistant to outliers. We then discuss the areas of study that are prevalent in the realm of the k-medians problem. Finally, we view an approach to the problem that has decreased its time complexity by instead performing the k-medians algorithm on small coresets representative of the data set.
AbstractList In this study, the general ideas surrounding the k-medians problem are discussed. This involves a look into what k-medians attempts to solve and how it goes about doing so. We take a look at why k-medians is used as opposed to its k-means counterpart, specifically how its robustness enables it to be far more resistant to outliers. We then discuss the areas of study that are prevalent in the realm of the k-medians problem. Finally, we view an approach to the problem that has decreased its time complexity by instead performing the k-medians algorithm on small coresets representative of the data set.
Author Harrell, Greg
Wang, Jin
Whelan, Christopher
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