An Attribute Weighted Fuzzy c-Means Algorithm for Incomplete Datasets Based on Statistical Imputation

The problem of missing data is frequently encountered in real world applications. In this paper, an attribute weighted fuzzy c-means algorithm for incomplete data sets is presented. The statistical representation proposed in our previous work is used here to impute the missing attribute values, and attribute weighting is involved to emphasize the contribution of important attributes. Experimental results indicate that the proposed approach has good clustering performance.