Algorithms for clustering data
Algorithms for clustering data
A procedure for ranking efficient units in data envelopment analysis
Management Science
Local convergence of tri-level alternating optimization
Neural, Parallel & Scientific Computations
Data Envelopment Analysis: A Comprehensive Text with Models, Applications References, and DEA-Solver Software with Cdrom
Cluster-Based Algorithms for Dealing with Missing Values
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Data envelopment analysis with missing values: an interval DEA approach
Applied Mathematics and Computation
Data Clustering: Theory, Algorithms, and Applications (ASA-SIAM Series on Statistics and Applied Probability)
Fuzzy clustering of incomplete data based on cluster dispersion
IPMU'10 Proceedings of the Computational intelligence for knowledge-based systems design, and 13th international conference on Information processing and management of uncertainty
Fuzzy c-means clustering of incomplete data
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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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.