## 2 Background and Literature Review
### Research Gap Analysis and Contributions  
DEA measure of energy use efficiency has two main benefits as compared to the old-style meaning, \"the proportion of energy services to energy input.\" At the initial step, DEA provides somewhere to stay several inputs (energy and no-energy inputs) and several outputs (desirable and undesirable) in the production progression. Next, DEA can also put up the purposes of DMUs in evaluating energy use efficiency. However, far too little attention has been paid to developing an efficient solution method to cope with DEA and data mining associated with finding the best model, algorithm and DMU. In conclusion, main contributions of this study can be summarized as follows:  
1. This research aims to study a comprehensive comparison of several efficiencies delivers insight into the firm's efficiency based on a novel machine learning approach combined with optimization models for Eco-efficiency Evaluation. This comparison is of considerable significance to cement companies' practitioners who desire to assess productivity and efficiency at a proper step of its progression.
2. An exclusive and easy to implement converting two-stage to single, standard and simple stage models in window analysis are applied with DEA SOLVER, which eventually results in comparing several efficient and inefficient DMUs. This model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. In fact, by converting two-stage to single proposed stage, desirable and undesirable inputs and output can be evaluated with simple CCR, BCC and FDH suggested model.
3. After applying the abovementioned optimization part, based on the nature of the data and attributes, the best filtering preprocess method have been chosen by experts. The best results are extracted, and the best fit preprocessing approach in machine learning section is introduced. DEA inputs and outputs as potential attributes for data mining clustering algorithms in WEKA are considered. In addition, data play the role of instances and finally efficient DMUs are applied for class yes and inefficient DMUs for class no.
4. After using the aforementioned novel combined optimization and machine learning approaches, the most efficient model, company and algorithm will be introduced. Thus, it can be beneficial for managers to conduct more effective processes.
5. Thus, in responding to this research gap, the following research questions are introduced and investigated: RQ1: What problem solution approaches can be developed to find appropriate decisions? RQ2: How can the robustness and accuracy of the designed approaches be demonstrated and evaluated, given a case study?  
To address the first question, according to the nature of the data and external inputs and final outputs in optimization step, the best fitting model in optimization part should be selected by expert judgment. To address the second question, data, external inputs and final outputs, efficient and inefficient DMUS in optimization step, play three important roles of instances, attributes and class yes or no in machine learning section respectively. Therefore, in order to achieve the highest accuracy, based on the above mentioned three elements in machine learning, the best fit preprocessing should be implemented. In this study, FDH model and three layers filtering preprocesses for the suggested clustering algorithms were the best scenario. Therefore, in future studies with particular data and attributes in different industries, the best scenario may have the filtering with less or more layers for the suggested algorithms. So, practitioners and experts, based on their previous experiences, should select the most appropriate approaches. In addition, other DEA models such as Slack Based Measure (SBM) may have more appropriate results.