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

Document Type : Original Article

Authors

1 PhD. Student, Department of Information Science and Knowledge, Information Retrieval, Tonekabon Branch, Islamic Azad University, Mazandaran, Iran.

2 Assistant Professor, Department of Information Science and Knowledge, Information Retrieval Orientation, Tonekabon Branch, Islamic Azad University, Mazandaran, Iran

3 Assistant Professor, Department of Information Science and Knowledge, Information Retrieval Orientation, Tonekabon Branch, Islamic Azad University, Mazandaran, Iran.

Abstract

Purpose: The main purpose of this article is to analyze the data of the Tehran Municipality websites and provide data mining solutions for managers' decisions.
Methodology: 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.
Findings: 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%.
Conclusion: 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.
 
 

Keywords

Main Subjects

References

Al Nuaimi, E.; Al Marzooqi, S. & Zaki, N. (2016). Predicting the decision for the provision of municipal services using data mining approaches. Paper presented at the 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC).
Amiri, A.; Deljoo, Gh.H. & Ghorbani Zadeh, V. (2008). Factors Affecting the Acceptance of Tehran Urban Management System (137) by Citizens. Journal of Urban Management, 6(22):
7-22. [In Persian]
Bakırlı, G.; Birant, D.; Mutlu, E.; Kut, A.; Denktaş, L. & Çetin, D. (2012). Data mining solutions for local municipalities. Paper presented at the Proceedings of the 12th European Conference on e-Government: ECEG.
Deshpande, S. & Thakare, V. (2010). Data mining system and applications: A review. International Journal of Distributed and Parallel systems (IJDPS), 1(1): 32-44.
Goebel, M. & Gruenwald, L. (1999). A survey of data mining and knowledge discovery software tools. ACM SIGKDD explorations newsletter, 1(1): 20-33.
Khoyqamat, K.; Safdari, N. & Yaghmaeifar, Sh. (2015). Sixth National Conference on Urban Planning and Management with emphasis on the components of the Islamic city. Mashhad: Mashhad Municipality. Mashhad: Islamic Council, Ferdowsi University of Mashhad. [In Persian]
Latha, P.P.; Madhuri, R.; Rao, K.P. & Bharathi, M.V. (2013). Effectual citizen relationship management with data mining techniques. GMR Institute of Technology, 2: 158-161.
Ling, C. & Delmelle, E.C. (2016). Classifying multidimensional trajectories of neighbourhood change: a self-organizing map and k-means approach. Annals of GIS, 22(3): 173-186.
Minaei Bidgoli, B.; Akhundzadeh, E.; Mousavi, M.H.; Ahmadvand, A.M. (2009). Using Data Mining in Citizen Relationship Management: A Case Study of Tehran Municipality 137 System. In: Electronic City Conference. [In Persian]
Mousavi Natanzi, M.A. (2020).What is Google Analytics and how does it analyze the site?. Available at: https://hamyar.co/what-is-google-analytics. [In Persian]
Qudusi, M.R.; Mahmoudi, J.; Musa Khani, M. & Jafari, M. (2016), Presenting the framework of knowledge governance in social networks. Bi-Quarterly Journal of Scientific Information Management, 5(1): 15-39. [In Persian]
Sharma, N.; Litoriya, R. & Sharma, A. (2020). Application and Analysis of K-Means Algorithms on a Decision Support Framework for Municipal Solid Waste Management. Paper presented at the International Conference on Advanced Machine Learning Technologies and Applications.
Solomon, S.; Nguyen, H.; Liebowitz, J. & Agresti, W. (2006). Using data mining to improve traffic safety programs. Industrial Management & Data Systems, 106(5): 621-643. 
DOI: 10.1108/02635570610666412.
Send comment about this article
CAPTCHA Image
Sciences and Techniques of Information Management
Volume 7, Issue 4
December 2021
Pages 93-114

Files

Share

How to cite

Statistics