An Integrated DEA and Data Mining Approach for Performance Assessment

Abstract

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 index, 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 exploring and discovering meaningful and hidden information from the vast databases. The results provide a set of rules that can be used by policy makers to explore reasons behind the progress and regress produc-tivities of DMUs.