An Improved Three-Way Clustering Based on Ensemble Strategy

An Improved Three-Way K-Means Algorithm by Optimizing Cluster Centers

Most of data set can be represented in an asymmetric matrix. How to mine the uncertain information from the matrix is the primary task of data processing. As a typical unsupervised learning method, three-way k-means clustering algorithm uses core region and fringe region to represent clusters, which can effectively deal with the problem of inaccurate decision-making caused by inaccurate information or insufficient data. However, same with k-means algorithm, three-way k-means also has the problems that the clustering results are dependent on the random selection of clustering centers and easy to fall into the problem of local optimization. In order to solve this problem, this paper presents an improved three-way k-means algorithm by integrating ant colony algorithm and three-way k-means. Through using the random probability selection strategy and the positive and negative feedback mechanism of pheromone in ant colony algorithm, the sensitivity of the three k-means clustering algorithms to the initial clustering center is optimized through continuous updating iterations, so as to avoid the clustering results easily falling into local optimization. Dynamically adjust the weights of the core domain and the boundary domain to avoid the influence of artificially set parameters on the clustering results. The experiments on UCI data sets show that the proposed algorithm can improve the performances of three-way k-means clustering results and is effective in revealing cluster structures.

Three-Way Ensemble Clustering Based on Sample’s Perturbation Theory

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.

TEXT Analysis on Ocean Engineering Equipment Industry Policies in China between 2010 and 2020

The ocean engineering equipment industry is the foundation for the implementation of maritime strategy. China’s national departments at all levels have developed relevant ocean engineering equipment industry policies to promote the rapid development of the industry. By using 56 industrial policies issued between 2010 and 2020 as the research sample, we conducted an in-depth assessment of the external structural characteristics and structure of the main cooperation network for such policies using descriptive statistics and social network analysis. Based on a symmetric analysis method, the two-dimensional matrix of cooperation breadth and cooperation depth, together with the measurement of the issuing subject’s centrality, was used to analyze the evolution of the subject’s role in the network. The research shows that the development of China’s ocean engineering equipment industry policies can be divided into three stages, and there are the following problems during the development of policies: (1) some policies and regulations are imperfect; (2) the network of cooperation among joint issuers is limited; and (3) some policies are issued by multiple government departments, but there is a lack of specialized and unified management from an absolute core department. Based on the above problems, we present some suggestions for policy optimization at the end of this paper.