Data Envelopment Analysis (DEA) is a multifactor productivity measurement tool and is used in assessing the relative efficiency of homogenous units. DEA assumes that the decision making unit (DMU) are homogenous in their environment and avoids any error or noise in measurements. Container terminals, which act as an interface between the sea and the shore, for loading and unloading of containers from ship to shore and vice-versa, may operate with its own attributes and goals. Every container terminal is characterized by some physical values that represent different relevant properties of the terminal. DEA, if employed alone, to measure the efficiency and set the bench mark for inefficient terminals gives biased result because all the container terminals may not be inherently similar. In order to overcome this shortcoming, in this paper, two important fields of information technology: data mining and data envelopment analysis is integrated to provide a new tool to appropriately set bench mark for inefficient terminals and prioritize the technical inputs that have the greatest impact needed to improve the inefficient terminals which otherwise is not possible with DEA alone.