Weighting Features for Partition around Medoids Using the Minkowski Metric

Renato Cordeiro De Amorim, Trevor Fenner

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    9 Citations (Scopus)


    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.
    Original languageEnglish
    Title of host publicationAdvances in Intelligent Data Analysis XI
    PublisherSpringer Nature
    ISBN (Electronic)978-3-642-34156-4
    ISBN (Print)978-3-642-34155-7
    Publication statusPublished - 2012
    Event11th Int Symposium, IDA 2012 - Helsinki, Finland
    Duration: 25 Oct 201227 Oct 2012

    Publication series

    NameLecture Notes in Computer Science


    Conference11th Int Symposium, IDA 2012


    Dive into the research topics of 'Weighting Features for Partition around Medoids Using the Minkowski Metric'. Together they form a unique fingerprint.

    Cite this