An integrated Data Envelopment Analysis and Data Mining for Performance assessment of Insurance branches

This paper presents a Data Envelopment Analysis (DEA) model combined with Bootstrapping to assess performance of one of the Data mining Algorithms. We used a two-step process for performance productivity analysis of Insurance Branches in Iran. First, using a Data Envelopment Analysis (DEA) model, the study analyses 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. DEA model uses a new Latent Variable radial input-oriented technology and simultaneously reduces inputs and undesirable outputs in a single multiple objective linear program.On the other hand, the classification and regression tree (C&R) efficiency model was then utilized to extract rules for exploring and discovering meaningful and hidden information from the vast databases.The conclusion of the combined model is a set of rules that can be used by policy makers to explore reasons behind the progress and regress productivities of DMUs.

Keywords:

Bootstrapping , Classification and Regression , Data envelopment Analysis , Malmquist Index , productivity