Data envelopment analysis of clinics with sparse data: fuzzy clustering approach

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Show simple item record Ben-Arieh, David Gullipalli, Deep Kumar 2012-09-26T14:00:34Z 2012-09-26T14:00:34Z 2012-09-26
dc.description.abstract 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. en_US
dc.relation.uri en_US
dc.subject Data Envelopment Analysis en_US
dc.subject Sparse data en_US
dc.subject Clustering en_US
dc.subject Fuzzy c-means en_US
dc.subject Healthcare en_US
dc.title Data envelopment analysis of clinics with sparse data: fuzzy clustering approach en_US
dc.type Article (author version) en_US 2012 en_US
dc.citation.doi doi:10.1016/j.cie.2012.01.009 en_US
dc.citation.epage 21 en_US
dc.citation.issue 1 en_US
dc.citation.jtitle Computers & Industrial Engineering en_US
dc.citation.spage 13 en_US
dc.citation.volume 63 en_US
dc.contributor.authoreid davidbe en_US
dc.contributor.authoreid deep en_US

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