Clustering algorithms are a form of unsupervised learning algorithm. With unsupervised learning, an algorithm is subjected to “unknown” data for which no previously defined categories or ...

Clustering data is the process of grouping items so that items in a group (cluster) are similar and items in different groups are dissimilar. After data has been clustered, the results can be analyzed ...

Wong's (1982) hybrid clustering method uses density estimates based on a preliminary cluster analysis by the k -means method. The preliminary clustering can be done by the FASTCLUS procedure, using ...

In practice, self-organizing map clustering is often used to validate k-means clustering. A relatively unexplored idea is to perform SOM clustering using an r-by-c map instead of a 1-by-k map. When ...

In this article we propose a variance estimator for the OLS estimator as well as for nonlinear estimators such as logit, probit, and GMM. This variance estimator enables cluster-robust inference when ...