Data Envelopment Analysis of clinics with sparse data: Fuzzy clustering approach

This paper presents a method for utilizing Data Envelopment Analysis (DEA) with sparse input and output data using fuzzy clustering concepts. DEA, a methodology to assess relative technical efficiency of production units is susceptible to missing data, thus, creating a need to supplement sparse data in a reliable and accurate manner. The approach presented is based on a modified fuzzy c-means clustering using optimal completion strategy (OCS) algorithm. This particular algorithm is sensitive to the initial values chosen to substitute missing values and also to the selected number of clusters. Therefore, this paper proposes an approach to estimate the missing values using the OCS algorithm, while considering the issue of initial values and cluster size. This approach is demonstrated on a real and complete dataset of 22 rural clinics in the State of Kansas, assuming varying levels of missing data. Results show the effect of the clustering based approach on the data recovered considering the amount and type of missing data. Moreover, the paper shows the effect that the recovered data has on the DEA scores.

► We present a new approach to recover missing data.
► The approach is accurate and robust, insensitive to the amount of data missing.
► We recommend a method to initialize the missing data.
► We developed an approach to select the desired number of clusters.
► We demonstrate the effect of generating the missing data on the DEA results.