Local linear modeling is a useful tool for knowledge discovery in databases and handling missing values is an important issue in real world application. It is an effective strategy to ignore only the missing values and use all the remaining elements of data matrices, and linear fuzzy clustering with missing values has been proposed, in which missing values are ignored by using least squares criterion based on lower rank approximation of data matrices. This paper considers extending the fuzzy clustering technique to the case where the missing values do not arise randomly. Two types of additional weights are introduced to the least squares criterion to adjust the effects of the number of the missing values. One is the weight for samples and the other is that for attributes. Numerical experiment shows that the weights are useful for handling missing values that do not arise randomly.
