Partitioning Around Medoids

The PAM algorithm searches for k representative objects in a data set (k medoids) and then assigns each object to the closest medoid in order to create clusters. Its aim is to minimize the sum of dissimilarities between the objects in a cluster and the center of the same cluster (medoid). It is known to be a robust version of k-means as it is considered to be less sensitive to outliers.

The Partitioning Around Medoids implemented in XLSTAT-R calls the pam function from the cluster package in R(Martin Maechler, Peter Rousseeuw, Anja Struyf, Mia Hubert). The function offers as well a useful tool to determine the number of called the silhouette plot.

About KCS

Kovach Computing Services (KCS) was founded in 1993 by Dr. Warren Kovach. The company specializes in the development and marketing of inexpensive and easy-to-use statistical software for scientists, as well as in data analysis consulting.

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