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.
In Indonesia, dengue fever is still common. This has caused many troubled people in Indonesia including Surabaya, East Java. Clinical signs of dengue start from mild illness to life-threatening shock syndrome. So it takes a development of a treatment to combat dengue fever. The purpose of this study is to see the prevalence of dengue fever in students. The prevalence of dengue fever at the University of Surabaya accounted for 17%, as we approached 81 people among them 14 were infected with dengue hemorrhagic fever. There were 42 (65%) people suffering from headaches, 19 (29%) joints, 26 (40%) muscle pain and 16 (25%) had abdominal pain, and from 64 among the 18-23 age group. The data obtained is helpful in determining the therapeutic approach to infections that appear. Furthermore, demographic factors such as age, gender, socioeconomic status with dengue virus transmission are also being considered.
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