TY - JOUR ID - 13103 TI - Fuzzy Semi-Parametric Partially Cluster-Wise Regression Analysis JO - Journal of Advanced Mathematical Modeling JA - JAMM LA - en SN - 2251-8088 AU - Asadollahi, Masoumeh AU - Akbari, Mohammad Ghasem AD - Department of Statistics, University of Birjand, Birjand, Iran AD - Y1 - 2017 PY - 2017 VL - 7 IS - 1 SP - 37 EP - 58 KW - Fuzzy number KW - α-pessimistic KW - Fuzzy clustering KW - One-stage generalized regression KW - Semi-parametric regression DO - 10.22055/jamm.2017.13103 N2 - Cluster analysis is one of the most important methods in classification in which the observations of each cluster has maximum similarity in terms of some desirable variables. In general the clustering methods are divided into two parts, crisp and fuzzy. In usual clustering methods an observation is in only one cluster whereas in fuzzy clustering it may fall into two or more clusters simultaneously. Yang and Ko (1996) introduced a fuzzy clustering method. Their method is an extension of the usual k-means clustering method as they assumed that the observations are fuzzy. A fuzzy regression model is used for studying the relationship between the explanatory variables and dependent variable. In some situations when some observations are dispersed and are heterogeneous, the regression model may not have a goodness of fit for data. To solve this problem Yang and Ko classified data and then based on fuzzy observations fitted a regression model to each cluster. In this paper we first explain the semi-parametric regression model introduced by Hesamian et al. [2017] and then use their model to perform our clustering method for fuzzy observations. Finally, based on some suggested goodness of fit criterions. We compare our results with those of Yang and Ko. UR - https://jamm.scu.ac.ir/article_13103.html L1 - https://jamm.scu.ac.ir/article_13103_1d73e3f296499806da22af06d6c18333.pdf ER -