K-MEAP: Multiple Exemplars Affinity Propagation With Specified $K$ Clusters

An improved density-based approach to risk assessment on railway investment

Purpose

Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.

Design/methodology/approach

First, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.

Findings

The IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.

Originality/value

This study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

A Novel Cluster Validity Index Based on Local Cores

It is critical to evaluate the quality of clusters for most cluster analysis. A number of cluster validity indexes have been proposed, such as the Silhouette and Davies-Bouldin indexes. However, these validity indexes cannot be used to process clusters with arbitrary shapes. Some researchers employ graph-based distance to cluster nonspherical data sets, but the computation of graph-based distances between all pairs of points in a data set is time-consuming. A potential solution is to select some representative points. Inspired by this idea, we propose a novel Local Cores-based Cluster Validity (LCCV) index to improve the performance of Silhouette index. Local cores, with local maximum density, are selected as representative points. Since graph-based distance is used to evaluate the dissimilarity between local cores, the LCCV index is effective for obtaining the optimal cluster number for data sets containing clusters with arbitrary shapes. Moreover, a hierarchical clustering algorithm based on the LCCV index is proposed. The experimental results on synthetic and real data sets indicate that the new index outperforms existing ones.