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 -