A novel fuzzy clustering algorithm with human-computer cooperation for incomplete data

Datasets with missing values are frequent in clustering analysis. It seems obvious that the reconstruction of missing attribute values can be considered as the key factors impacting the clustering performance. For this, a FCM clustering algorithm for incomplete data sets based on human-computer cooperation is proposed in this paper. On account of the uncertainty of missing attributes, intervals are introduced to the missing attributes based on the nearest-neighbor rule. Furthermore, the corresponding iterative solution approach is developed for calculating the missing attributes based on the optimal completion strategy (OCS) and compulsion strategy. The experimental results of several data sets can demonstrate the superiority of the proposed algorithm.