Soma Gholamveisy,



citizen management,data mining,RFM-SOM algorithm,Apriori algorithm,a new feature ,


Significant revolution in different organizations chief’s point of view toward customer treating and the level of product presentation or services resulted in redefining the structure of these organizations based on this point of view. The municipal services are very important as well. The strategy of “CRM” which was so successful in the private sector and has been applying as “CiRM” in the public sector of developed countries could be very useful for this achievement. The main goal of citizen management is realizing the citizen's needs and demands, improving communication through connection with citizens and optimizing it to increase the level of their satisfaction. The government agencies do it based on their idea and point of view cause the citizen are valuable assets in the planning of services and reduction of costs. This study proposes a combined data mining method to discover hidden knowledge in call citizen compliant of the municipality of Tehran. A Self-organizing map neural network was used to identifying and classifying citizen needs based on RFM analysis. It also classified citizen needs into three majors. the result of classification and clustering of SOM has created a new feature to profiled call’s customer to identify temporal-spatial patterns of problems by using an association rule with the Apriori algorithm. The results of this idea demonstrate that accordance of citizens call compliant in a different area and discovering hidden knowledge can facilitate the performance of human recourse in improving services to citizens.


I. Anthony Danna & Oscar H. Gandy Jr. (2000) .All That Glitters is Not Gold: Digging Beneath the Surface of Data mining. Journal of Business Eithics 373-386.
II. Ahmadvand .A (2010)hybrid data mining model for effective citizen relationship management : a case study on theran municipalit, International Conference on e-ducation.e-business.e-management and learning. Iran
III. Akhondzadeh-Noughabi.E. (2013). FTiS:A new model for effective urban management : A case study of urban systems in iran. Cities, pp.394-403.
IV. Akhondzadeh-Noughabi.E, Amin-Naseri, A. Albadvi. And Saeedi. M (2016). Human resource performance evaluation from CRM perspective: a two-step association rule analysis. Int. J. Business Performance Management, 17. 1
V. Agrawal .Rand. Srikant.R (1994) ‘Fast algorithms for mining association rules’, Proc. 20th Int. Conf. Very Large Data Bases, VLDB, pp.487–499

VI. Buckinxa,W.(2004). Customer-adapted coupon targeting using feature selection, . Expert Systems with Application,pp 509-518
VII. Chang L,Che-Wei(2009). Mining the text information to optimizing the customer relationship management,. Expet systems with Applications 1443 -1433.
VIII. Ghodousi, M, Alesheikh, A, Saeidian, B. Pradhan and G. Lee. (2019). Evaluating Citizen Satisfaction and Prioritizing Their Needs Based on Citizens’ Complaint Data Sustainability 2019, 11, 459.
IX. Ching Z. X. (2004,). Mining class outliers: concepts ,algorithms and applications in CRM ,. Expert systems with Applications 681-69
X. Han. J and kamber. A (2001) Data Mining: Concepts and Techniques, p.5, Morgan Kaufmann, San Francisco, CA
XI. Hughes. A.M (1994). Strategic database marketing. Chicago: Probus Publishing Company
XII. J. Dunn. : Well separated clusters and optimal fuzzy partitions. 4. 95-104. 1974
XIII. H. H. Chen, Wud. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques Expert Systems with Applications 40 ,7513–7518
XIV. R. Liu. D,(2005). Integrating AHP and data mining for product recommomerendation based on customer lifetime value ,. information and Management 42, 340-387.
XV. Hsieh, N.C. (2004.) An integrated data mining and behavioral scoring model for analyzing bank customers, Expert Systems with Applications 27 623–633
XVI. Redick C. GR (2004). A two- stage model of e-government growth: Theories and empiirical evidence for U.S cities,. Government Information Quarterly, 21:51-64.
XVII. Reddick C. G.(2009). The aoption of centralized customer service systems: A survey of local governments,. Goverment Information Quarte 26: -226
XVIII. Schellong. A. L. (2007).Managing citizen relationships in disasters :. proceedings of the 40th annual Hawaii international conference on system sciences. Hurricane Wilma,311 and Miami-Dade country.
XIX. Schellong. A. (2005 (CRM in the public sector: towords a conceptual research framework. national conferance on digital government research. Atlanta,Georgia,.
XX. Silva.R. (2007) Boosting goverment reputation through CRM. The international journal of public Sector Management, (7):588-6.
XXI. Sasaki.Takanori A. (2007) An Empirical study on citizen Relationship Management in japan,.
XXII. Srinivas D., K. Rajkumar, N. Hanumantha Rao. : ‘ SERVICE QUALITY DIMENSIONS-A STUDY OF SELECT PUBLIC AND PRIVATE SECTOR BANKS OF WARANGAL DISTRICT’. J. Mech. Cont. & Math. Sci., Vol.-15, No.-8, August (2020) pp 307-314. DOI : 10.26782/jmcms.2020.08.00029.
XXIII. Tan, P.N. Steinbach M.and. Kumar (2006) Introduction to Data Mining, Pearson Education Inc., US
XXIV. Taniar. D (2008) Data Mining and Knowledge Discovery Technologies, IGI Global, New York.

XXV. Vellido, A. Lisboa, P. J. G., & Vaughan. J (1999). neural networks in business: a survey of applications (1992–1998). Expert Systems with Applications, 17, 51–70.

XXVI. Zayyanu Umar, Agozie Eneh, Okereke George E. : ‘JOINED HETEROGENEOUS CLOUDS RESOURCES MANAGEMENT: AN ALGORITHM DESIGN’. J. Mech. Cont.& Math. Sci., Vol.-15, No.-8, August (2020) pp 39-52. DOI: 10.26782/jmcms.2020.08.00005

View Download