A Hybrid Data Mining Model for Effective Citizen Relationship Management: A Case Study on Tehran Municipality

Ahmadvand, A M; Minaei-Bidgoli, Behrouz; Akhondzadeh, Elham

doi:10.1109/ic4e.2010.114

Cited by 6 publications

(2 citation statements)

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“…In their study, the problems were firstly grouped by a K-means algorithm, and then the information related to citizens' satisfaction with the services were analyzed by using association rules and an a priori algorithm. In addition, the relation between the parameters of citizens' satisfaction with quality and speed of services was considered and also effective factors and conditions affecting the satisfaction and dissatisfaction of the citizens were determined [12]. Minaei-Bidgoli and Akhondzadeh did similar research to the previous one on '137 databases' of Tehran municipality.…”

Section: Introductionmentioning

confidence: 90%

“…According to Figure 3, two methods were executed. In the first method, similar to previous studies [12,17], concerning the prioritization of urban needs and estimation of satisfaction, needs were prioritized. First, this it was carried out according to the two factors of frequency and recency, and then the estimation of satisfaction was performed.…”

Section: Methodsmentioning

confidence: 99%

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Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data

et al. 2019

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Citizen Relationship Management (CiRM) is one of the important matters in citizen-centric e-government. In fact, the most important purpose of e-government is to satisfy citizens. The ‘137 system’ is one of the most important ones based on the citizen-centric that is a municipality phone based request/response system. The aim of this research is a data-mining of a ‘137 system’ (citizens’ complaint system) of the first district of Bojnourd municipality in Iran, to prioritize the urban needs and to estimate citizens’ satisfaction. To reach this, the K-means and Bees Algorithms (BA) were used. Each of these two algorithms was executed using two different methods. In the first method, prioritization and estimation of satisfaction were done separately, whereas in the second method, prioritization and estimation of satisfaction were done simultaneously. To compare the clustering results in the two methods, an index was presented quantitatively. The results showed the superiority of the second method. The index of the second method for the first needs in K-means was 0.299 more than the first method and it was the same in two methods in BA. Also, the results of the BA clustering were better at it because of the S (silhouette) and CH (Calinski-Harabasz) indexes. Considering the final prioritization done by the two algorithms in two methods, the primary needs included asphalt, so specific schemes should be considered.

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Section: Introductionmentioning

confidence: 90%

Section: Methodsmentioning

confidence: 99%