WebDec 15, 2024 · ( A ) Illustrate the strength and weakness of k-means in comparison with thek-medoidsalgorithm . The k-medoids algorithem is more robust than k-means in the presence of noise and outlier , beacuse a medoid is less influenced by outlier or other extreme values than a mean . WebNov 24, 2024 · Suitable in a large dataset: K-means is suitable for a large number of datasets and it’s computed much faster than the smaller dataset. It can also produce …
Solved What is K-means from a basic standpoint? What are the
WebFeb 14, 2013 · 1) If variables are huge, then K-Means most of the times computationally faster than hierarchical clustering, if we keep k smalls. 2) K-Means produce tighter clusters than hierarchical clustering, especially if the clusters are globular. K-Means Disadvantages : 1) Difficult to predict K-Value. 2) With global cluster, it didn't work well. WebAug 12, 2015 · K-mediods is an improvement of K-means to deal with discrete data, which takes the data point, most near the center of data points, as the representative of the corresponding cluster. The typical … jeff salamon
Merton’s Strain Theory of Deviance and Anomie in Sociology
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