A Three-Way Decisions Clustering Algorithm for Incomplete Data

Clustering is one of the most widely used efficient approaches in data mining to find potential data structure. However, there are some reasons to cause the missing values in real data sets such as difficulties and limitations of data acquisition and random noises. Most of clustering methods can't be used to deal with incomplete data sets for clustering analysis directly. For this reason, this paper proposes a three-way decisions clustering algorithm for incomplete data based on attribute significance and miss rate. Three-way decisions with interval sets naturally partition a cluster into positive region, boundary region and negative region, which has the advantage of dealing with soft clustering. First, the data set is divided into four parts such as sufficient data, valuable data, inadequate data and invalid data, according to the domain knowledge about the attribute significance and miss rate. Second, different strategies are devised to handle the four types based on three-way decisions. The experimental results on some data sets show preliminarily the effectiveness of the proposed algorithm.