Abstract
This paper introduces a new hybrid clustering method using Data Envelopment Analysis (DEA) and Balanced Scorecard (BSC) methods. DEA cannot identify its’ input and output itself, and it is a major weakness of the DEA. In the proposed method, this gap is resolved by integrating DEA with BSC. Some decision-making units (DMUs) needed in DEA method, in compliance with some inputs and outputs is the major drawback of this integration. To deal with this disadvantage, the proposed method selects the most important strategic factors, attained from the BSC method. These data considered to be the input data for the DEA method to calculate relative closeness (RC) of each DMU to the ideal one. Plotting the screen diagram regarding RC index leads us to the final clustering method. Finally, computational results show the applicability and usefulness of the method.
Highlights
AND LITERATURE REVIEWClustering is a statistical method to divide similar objects into the same bunches
Many algorithms are proposed to cluster data based on minimizing total dissimilarity (Po, Guh, & Yang, 2009), such as hard C-means (HCM) (Ross, 2009), fuzzy C-means (FCM) (Barrios, Villanueva, Cavazos, & Colas, 2016), possibilistic C-means (PCM) (Škrjanc & Dovžan, 2015), interval Type-2 fuzzy possibilistic C-means clustering algorithm (Rubio, Castillo, & Melin, 2015), multiple kernels interval Type-2 possibilistic Cmeans (Vu & Ngo, 2016), and so on
This paper introduces a new hybrid clustering method using data envelopment analysis (DEA) and balanced scorecard (BSC) methods
Summary
Clustering is a statistical method to divide similar objects into the same bunches. There is a vast literature in the field of clustering and there have been attempts to categorize these researches. Fahad et al (2014) introduce concepts and algorithms related to the area of clustering as well as providing a comparison, from a theoretical and from an empirical perspective. The method uses this approach, but applies Balanced Scorecard (BSC) to introduce the outputs and rather the inputs to have a complete comprehensive method for organizational clustering. It is clear that the focus of these scholars is on the performance assessment; this paper employs this combination to introduce a new powerful method for clustering. This makes the traditional methods to be complex and impractical It is the superiority of the proposed method that is not be limited by this principle and may be used for ranking and clustering the DMUs with any number of outputs or inputs. The factors are used as the input data for the DEA method to make ranking and clustering in any organizations with and number of sub-organizations
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