Study of Various Data Mining Methods to Select the Appropriate Method for Managers to Make Decisions in Urban Management (Case Study: Tehran Municipality)

The main purpose of this article is to analyze the data of the Tehran Municipality websites and provide data mining solutions for managers' decisions.

This research is fundamental and in terms of nature, it can be considered analytical. The data collection method was the field. The statistical population was selected from 220 domains of Tehran Municipality and for analysis, data mining techniques were used to discover the appropriate decision model of city managers. The source of data collection was web analytics and tools used by Google Analytics.

The accuracy of the LSTM deep neural network is 99.84%. Network accuracy is 99.90%, the call is equal to 99.63%, the error is equal to 0.16%, MSM standard is equal to 0.003. The accuracy of the DBScan method with other basic methods for analyzing the data of Tehran Municipality websites is 99.84%, the deep learning method is 99.25%, the nearest neighbor method is 99.81% and the decision tree method is equal to 99.8%.With these interpretations, the rate of improvement of the accuracy of the DBScan method in comparison with the deep learning methods is equal to 0.59%.

Finally, by simulating the DBScan method to identify and analyze the data of Tehran Municipality websites and provide data mining solutions for managers' decisions, it was observed that the proposed method provides suggestions to managers to improve site visits and The performance of the municipality is significantly effective.