Measuring Performance by Integrating K-Medoids With Dea: Mongolian Case

Abstract

Performance measurement encourages Decision Making Units (DMUs) to improve their level of performance by comparing their current financial positions with that of their peers. Data Envelopment Analysis (DEA) is a widely used approach to performance measurement, though it is susceptible when the data is heterogeneous. The main objective of this study is to examine the performance of Mongolian listed companies by combining DEA and a k-medoid clustering method. Clustering facilitates the characterization and patterns of data and identification of homogenous groups. This study applies the integration of k-medoids and performance measurement. The research used 89 Mongolian companies' financial statements from 2012 to 2015 -obtained from the Mongolian Stock Exchange website. The companies are grouped by k-medoids clustering, and efficiency of each cluster is evaluated by DEA. According to the silhouette method, the companies are classified into two clusters which are considered first cluster as small and medium-sized (80), and second cluster as big (9) companies. Both clusters are analyzed and compared by financial ratios. The mean efficiency score of big companies' is much higher than that of small and medium-sized companies. Integrated results show that cluster-specific efficiency provides better performance than pre-clustering efficiency results.Keywords: financial performance, k-medoids clustering, data envelopment analysis, input efficiency, variable return to scale, decision making unit. JEL Classification: C38, C14, L25.ing (finding meaningful groups of objects that share common characteristics) and utility (to abstract the representative object from among many others in the same clusters) (Wu, 2012).Clustering techniques are divided into partitional and hierarchical types. The most popular and well-known partitional cluster technique is k-means, which is widely employed in research. Although k-means is a popular choice among partitional clusters, it is sensitive to outliers. On the contrary, the k-medoids algorithm is more robust and less sensitive to outliers. Research, which compared k-medoids with k-means, suggested the k-medoid was better in all aspects. For example, Arora and Varshney (2016) compared k-means and k-medoids in their research. Their results proved that k-medoids is better than k-means; as execution time, sensitivity to outliers and space complexity of overlapping are all less. Narayana and Vasumathi (2018) stated in their work that the k-medoids technique is more accurate and easier to understand than k-means clustering. Moreover, Patel and Singh (2013) studied a new approach for k-means and k-medoids algorithm and concluded that k-medoids improved accuracy. However, Arbin, Suhaimi, Mokhtar, and Othman (2016) evaluated k-means and k-medoids, and both methods were found to be good having mean errors less than three.The K-medoids algorithm, which was proposed by Kaufman and Rousseeuw (1987), was developed and investigated by various researchers from different fields. For example, Ho-Kieu, Vo-Van...

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