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

被引：2

作者：

Rustam ^{[1
]}

Usman, Koredianto ^{[1
]}

Kamaruddin, Mudyawati ^{[2
]}

Chamidah, Dina ^{[3
]}

Nopendri ^{[4
]}

Saleh, Khaerudin ^{[1
]}

Eliskar, Yulinda ^{[1
]}

Marzuki, Ismail ^{[5
]}

机构：

[1] Telkom Univ, Sch Elect Engn, Dept Telecommun Engn, Jl Telekomunikasi 1 Dayeuh Kolot, Kabupaten Bandung 40257, Jawa Barat, Indonesia

[2] Univ Muhammadiyah Semarang, Fac Hlth Sci, Semarang, Jawa Tengah, Indonesia

[3] Univ Wijaya Kusuma Surabaya, Fac Language & Sci, Dept Biol Educ, Surabaya, Jawa Timur, Indonesia

[4] Telkom Univ, Sch Ind Engn, Dept Ind Engn, Jawa Barat, Indonesia

[5] Fajar Univ, Dept Chem Engn, Makassar, Sulawesi Selata, Indonesia

来源：

ACTA POLYTECHNICA | 2021年 / 61卷 / 02期

关键词：

Incomplete data; fuzzy clustering; possibilistic clustering; missing values imputation; VALIDITY INDEX;

D O I：

10.14311/AP.2021.61.0364

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

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.

引用

页码：364 / 377

页数：14

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