## 6 Discussion  
The efficiency of the projected method delivers us with a chance to distinguish pattern recognition of the whole, combining DEA and data mining techniques during the selected period (six years over 2014-2019). Meanwhile, the cement industry is one of the foremost manufacturers of naturally harmful material using an undesirable by-product; specific stress is given to that pollution control investment or undesirable output while evaluating energy use efficiency. The significant concentration of the study is to respond to four preliminary questions. First, whether the conversion of the two-stage model to a simple and standard single-stage model has any positive impacts on Eco-efficiency evaluation? Secondly, whether FDH proposed model has any positive effects on Eco-efficiency? Thirdly, whether combining DEA and data mining have any positive effect on Eco-efficiency evaluation? Fourthly, what are the advantages of clustering three layers filtering pre-processing suggested method?  
To answer the first question, this model is proposed to fix the efficiency of a two-stage process and prevent the dependency on various weights. Decreasing pollutant material investment, as well as energy consumption (inputs of the single proposed model) and increasing waste material removed as well as cement production (outputs of the unique proposed model) have a positive influence on efficiency of cement companies.  
For the second question, one of the interesting features of FDH model due to nonconvexity nature of FDH efficiency frontier is that, in FDH model, targets link to experiential units which is more well-matched with real life because, in some conditions, the practical unit is much better when compared with a real unit rather than with a simulated one. Meanwhile, unlike CCR and BCC models, the FDH model does not run with the convexity hypothesis. Consequently, this model has a separate nature which means the efficient mark point for an inefficient DMU only to be allocated as a point between individual pragmatic DMUs. Therefore, the efficiency analysis is completed comparative to the other assumed DMUs as a replacement for a hypothetical efficiency frontier. This has the benefit that the accomplishment target for an inefficient DMU assumed by its efficient target point will be more reliable than in cases of CCR and BCC models.  
To answer the third question, after efficiency evaluation in window analysis and introducing the highest performance company, data mining clustering algorithms play an important role to find the superior model and algorithm.  
For the last question, in order to have more appropriate data, three layers filtering pre-processing which suggested by experts remove unrelated data and attributes, increase the accuracy of the whole system in each step and plays an important role in the quality of algorithms. Consequently, it is so important to extensively compare tactics in many possible states. Therefore, a methodical assessment of three recognized clustering algorithms was applied to exactly find the best algorithm. Therefore, comparing clustering algorithms in the current study provides a valuable comparison of the various data mining strong rules.