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.

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

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