An Integrated DEA and Data Mining Approach for Performance Assessment

This paper presents a data envelopment analysis (DEA) model combined with Bootstrapping to assess performance of one of the Data mining Algorithms. We applied a two-step process for performance productivity analysis of insurance branches within a case study. First, using a DEA model, the study analyzes the productivity of eighteen decision- making units (DMUs). Using a Malmquist in-dex, DEA determines the productivity scores but cannot give details of factors depend on regress and progress productivity. The proposed model presents anew latent variable radial input-oriented technology and simultaneously reduces inputs and undesirable outputs in a single multiple objective linear programming. On the other hand, classification and regression tree allow DMU to extract rules for ex-ploring and discovering meaningful and hidden information from the vast data-bases. The results provide a set of rules that can be used by policy makers to explore reasons behind the progress and regress productivities of DMUs.