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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Weighting Features for Partition around Medoids Using the Minkowski Metric
verfasst von : Renato Cordeiro de Amorim, Trevor Fenner
Erschienen in: Advances in Intelligent Data Analysis XI
Verlag: Springer Berlin Heidelberg
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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.