  The banking industry is very important for an economic cycle of each country
and provides some quality of services for us. With the advancement in
technology and rapidly increasing of the complexity of the business
environment, it has become more competitive than the past so that efficiency
analysis in the banking industry attracts much attention in recent years. From
many aspects, such analyses at the branch level are more desirable. Evaluating
the branch performance with the purpose of eliminating deficiency can be a
crucial issue for branch managers to measure branch efficiency. This work not
only can lead to a better understanding of bank branch performance but also
give further information to enhance managerial decisions to recognize
problematic areas. To achieve this purpose, this study presents an integrated
approach based on Data Envelopment Analysis (DEA), Clustering algorithms and
Polynomial Pattern Classifier for constructing a classifier to identify a class
of bank branches. First, the efficiency estimates of individual branches are
evaluated by using the DEA approach. Next, when the range and number of classes
were identified by experts, the number of clusters is identified by an
agglomerative hierarchical clustering algorithm based on some statistical
methods. Next, we divide our raw data into k clusters By means of
self-organizing map (SOM) neural networks. Finally, all clusters are fed into
the reduced multivariate polynomial model to predict the classes of data.
