Weighting Features for Partition around Medoids Using the Minkowski Metric
نویسندگان
چکیده
In this paper we introduce the Minkowski weighted partition around medoids algorithm (MW-PAM). This extends the popular partition around medoids algorithm (PAM) by automatically assigning K weights to each feature in a dataset, where K is the number of clusters. Our approach utilizes the within-cluster variance of features to calculate the weights and uses the Minkowski metric. We show through many experiments that MW-PAM, particularly when initialized with the Build algorithm (also using the Minkowski metric), is superior to other medoid-based algorithms in terms of both accuracy and identification of irrelevant features.
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