关键词: 不完备数据, 三支决策聚类, q近邻

Abstract: Clustering is a common technique for data analysis, and has been widely used in many practical areas. However, in many practical applications, 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 decision clustering algorithm for incomplete data based on q-nearest neighbors. Firstly, the algorithm finds the q-nearest neighbors for an object with missing values, and the missing value is filled by the average value of q-nearest neighbors. Secondly, it uses the clustering method based on density peaks for the complete data set to obtain the clustering result. For the data object with uncertainty in each cluster, it is designed to the boundary region of a cluster using the three-way decision theory. The three-way decision with interval sets naturally partitions a cluster into three regions as the positive region, boundary region and negative region, which has the advantage of dealing with soft clustering. The experimental results on some UCI data sets and synthetic data sets show preliminarily the effectiveness of the proposed algorithm.

Key words: incomplete data, three-way decision clustering, q-nearest neighbors