1. Degirmenci, T., Ozbakır, L.: Differentiating households to analyze consumption patterns: a data mining study on official household budget data. WIREs Data Mining Knowl. Discov. 8(1), 1–15 (2017)
2. Palarea-Albaladejo, J., Fernández, J.A.M., Soto, J.: Dealing with distances and transformations for fuzz c-means clustering of compositional data. J. Classif. 29(2), 144–169 (2012)
3. Sun, H., Wang, S., Jiang, Q.: FCM-based model selection algorithms for determining the number of clusters. Pattern Recogn. 37(10), 2027–2037 (2004)
4. Kumar, K.M., Reddy, A.R.M.: An efficient k-means clustering filtering algorithm using density based initial cluster centers. Inf. Sci. 418–419, 286–301 (2017)
5. Husein, A.M., Harahap, M., Aisyah, S., Purba, W., Muhazir, A.: The implementation of two stages clustering (k-means clustering and adaptive neuro fuzzy inference system) for prediction of medicine need based on medical data. J. Phys: Conf. Ser. 978(1), 012019 (2017)