In this paper, we propose a weighted principal component analysis (WPCA) 
using the result of fuzzy clustering \cite{be2}.  
The principal component analysis (PCA) \cite{an}, \cite{jo} is one widely 
used and well-known data analysis method.  
However there is a problem, when the data does not have a structure 
that the PCA can capture  we cannot obtain any satisfactory 
results. For the most part, this is due to the uniformity of the data structure,
which means we cannot find any significant proportion or accumulated 
proportion for the obtained principal components.\par
In order to solve this problem, we use the cluster structure and degree 
of belongingness of objects to clusters,  
which is obtained as the fuzzy clustering result. 
By the introduction of the pre-classification  and the degree 
of belongingness to the data, we can transform the data 
into a clearer structured data,  so avoiding the noise in the data.