MODIFIED POSSIBILISTIC FUZZY C-MEANS ALGORITHM FOR CLUSTERING INCOMPLETE DATA SETS

MODIFIED POSSIBILISTIC FUZZY C-MEANS ALGORITHM FOR CLUSTERING INCOMPLETE DATA SETS

A possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm proposed to deal with the weaknesses associated with handling noise sensitivity and coincidence clusters in fuzzy c-means (FCM) and possibilistic c-means (PCM). However, the PFCM algorithm is only applicable to complete data sets...

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Bibliographic Details
Main Authors: Rustam, Koredianto Usman, Mudyawati Kamaruddin, Dina Chamidah, Nopendri, Khaerudin Saleh, Yulinda Eliskar, Ismail Marzuki
Format: Article
Language:English
Published: CTU Central Library 2021-04-01
Series:Acta Polytechnica
Subjects:
incomplete data
fuzzy clustering
possibilistic clustering
missing values imputation.
Online Access:https://ojs.cvut.cz/ojs/index.php/ap/article/view/6763
Description
Summary:A possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm proposed to deal with the weaknesses associated with handling noise sensitivity and coincidence clusters in fuzzy c-means (FCM) and possibilistic c-means (PCM). However, the PFCM algorithm is only applicable to complete data sets. Therefore, this research modified the PFCM for clustering incomplete data sets to OCSPFCM and NPSPFCM with the performance evaluated based on three aspects, 1) accuracy percentage, 2) the number of iterations, and 3) centroid errors. The results showed that the NPSPFCM outperforms the OCSPFCM with missing values ranging from 5% − 30% for all experimental data sets. Furthermore, both algorithms provide average accuracies between 97.75%−78.98% and 98.86%−92.49%, respectively.
ISSN:1210-2709
1805-2363

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