As a powerful data analysis technique, clustering plays an important role in data mining. Traditional hard clustering uses one set with a crisp boundary to represent a cluster, which cannot solve the problem of inaccurate decision-making caused by inaccurate information or insufficient data. In order to solve this problem, three-way clustering was presented to show the uncertainty information in the dataset by adding the concept of fringe region. In this paper, we present an improved three-way clustering algorithm based on an ensemble strategy. Different to the existing clustering ensemble methods by using various clustering algorithms to produce the base clustering results, the proposed algorithm randomly extracts a feature subset of samples and uses the traditional clustering algorithm to obtain the diverse base clustering results. Based on the base clustering results, labels matching is used to align all clustering results in a given order and voting method is used to obtain the core region and the fringe region of the three way clustering. The proposed algorithm can be applied on the top of any existing hard clustering algorithm to generate the base clustering results. As examples for demonstration, we apply the proposed algorithm on the top of K-means and spectral clustering, respectively. The experimental results show that the proposed algorithm is effective in revealing cluster structures.

The complexity of the data type and distribution leads to the increase in uncertainty in the relationship between samples, which brings challenges to effectively mining the potential cluster structure of data. Ensemble clustering aims to obtain a unified cluster division by fusing multiple different base clustering results. This paper proposes a three-way ensemble clustering algorithm based on sample’s perturbation theory to solve the problem of inaccurate decision making caused by inaccurate information or insufficient data. The algorithm first combines the natural nearest neighbor algorithm to generate two sets of perturbed data sets, randomly extracts the feature subsets of the samples, and uses the traditional clustering algorithm to obtain different base clusters. The sample’s stability is obtained by using the co-association matrix and determinacy function, and then the samples can be divided into a stable region and unstable region according to a threshold for the sample’s stability. The stable region consists of high-stability samples and is divided into the core region of each cluster using the K-means algorithm. The unstable region consists of low-stability samples and is assigned to the fringe regions of each cluster. Therefore, a three-way clustering result is formed. The experimental results show that the proposed algorithm in this paper can obtain better clustering results compared with other clustering ensemble algorithms on the UCI Machine Learning Repository data set, and can effectively reveal the clustering structure.

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