Skip to main content
Log in

Modified dynamic fuzzy c-means clustering algorithm – Application in dynamic customer segmentation

  • Published:
Applied Intelligence Aims and scope Submit manuscript

Abstract

The dynamic customer segmentation (DCS) is a useful tool for managers in implementing marketing strategies by observing dynamic changes that are happening in the customer segments over time. The Crespo’s dynamic fuzzy c-means (CDFCM) is one of the clustering algorithms introduced in the literature for DCS. We have suggested modifications to the CDFCM algorithm owing to certain shortcomings found in it, resulting in the modified dynamic fuzzy c-means (MDFCM) algorithm. To show the performance of the MDFCM algorithm, extensive experiments were carried out in comparison with the CDFCM algorithm using a retail supermarket dataset with eleven new data updates. To validate the results of the MDFCM algorithm, the fuzzy clustering evaluation measures such as Xie-Beni (XB) index, within sum of squared error (WSSE), root mean squared error (RMSE), Kwon index, and Tang index are utilized. The experimental results show that MDFCM is the most effective clustering algorithm for DCS, and the results are tested statistically to show its significance. The MDFCM algorithm is further compared with another successful algorithm available in the literature called Fathabadi’s dynamic fuzzy c-means (FDFCM). To show the usefulness of the MDFCM algorithm, a DCS framework is proposed and it has been demonstrated through a case study.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Smith WR (1956) Product Differentiation and Market Segmentation as Alternative Marketing Strategies. J Mark 21:3. https://doi.org/10.2307/1247695

    Article  Google Scholar 

  2. Nairn A, Berthon P (2003) Creating the Customer: The Influence of Advertising on Consumer Market Segments - Evidence and Ethics. J Bus Ethics 42:83–99. https://doi.org/10.1023/A:1021620825950

    Article  Google Scholar 

  3. Ngai EWT, Xiu L, Chau DCK (2009) Application of data mining techniques in customer relationship management: A literature review and classification. Expert Syst Appl 36:2592–2602. https://doi.org/10.1016/j.eswa.2008.02.021

    Article  Google Scholar 

  4. Chen Y, Zhang G, Hu D, Wang S (2006) Customer segmentation in customer relationship management based on data mining. IFIP Int Fed Inf Process 207:288–293. https://doi.org/10.1007/0-387-34403-9_40

    Article  Google Scholar 

  5. Hiziroglu A (2013) Soft computing applications in customer segmentation: State-of-art review and critique. Expert Syst Appl 40:6491–6507. https://doi.org/10.1016/j.eswa.2013.05.052

    Article  Google Scholar 

  6. Andaleeb SS (2016) Market Segmentation. Targeting and Positioning Strateg Mark Manag Asia:179–207. https://doi.org/10.1108/978-1-78635-746-520161006

  7. Hughes A (1994) Strategic database marketing. Probus Publ, Chicago

    Google Scholar 

  8. Aggelis V, Christodoulakis D (2005) Customer Clustering using RFM analysis. Proc 9th WSEAS … 1–5

  9. Cheng CH, Chen YS (2009) Classifying the segmentation of customer value via RFM model and RS theory. Expert Syst Appl 36:4176–4184. https://doi.org/10.1016/j.eswa.2008.04.003

    Article  Google Scholar 

  10. Fruchter GE, Zhang ZJ (2004) Dynamic Targeted Promotions: A Customer Retention and Acquisition Perspective. J Serv Res 7:3–19. https://doi.org/10.1177/1094670504266130

    Article  Google Scholar 

  11. Kaya O (2013) Dynamic pricing of durable products with heterogeneous customers and demand interactions over time. Comput Ind Eng 65:679–688. https://doi.org/10.1016/j.cie.2013.05.014

    Article  Google Scholar 

  12. Bernstein F, Modaresi S, Sauré D (2016) A Dynamic Clustering Approach to Data-Driven Assortment Personalization. 1–39. https://doi.org/10.2139/ssrn.2983207

  13. Lingras P, Hogo M, Snorek M, West C (2005) Temporal analysis of clusters of supermarket customers: Conventional versus interval set approach. Inf Sci (Ny) 172:215–240. https://doi.org/10.1016/j.ins.2004.12.007

    Article  Google Scholar 

  14. Gür Ali Ö, Aritürk U (2014) Dynamic churn prediction framework with more effective use of rare event data: The case of private banking. Expert Syst Appl 41:7889–7903. https://doi.org/10.1016/j.eswa.2014.06.018

    Article  Google Scholar 

  15. Crespo F, Weber R (2005) A methodology for dynamic data mining based on fuzzy clustering. Fuzzy Sets Syst 150:267–284. https://doi.org/10.1016/j.fss.2004.03.028

    Article  MathSciNet  MATH  Google Scholar 

  16. Bagnall A, Lines J, Bostrom A et al (2017) The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances. Data Min Knowl Disc 31:606–660. https://doi.org/10.1007/s10618-016-0483-9

    Article  MathSciNet  Google Scholar 

  17. Shukri S, Faris H, Aljarah I et al (2018) Evolutionary static and dynamic clustering algorithms based on multi-verse optimizer. Eng Appl Artif Intell 72:54–66. https://doi.org/10.1016/j.engappai.2018.03.013

    Article  Google Scholar 

  18. Webber R (2013) The evolution of direct, data and digital marketing. J Direct Data Digit Mark Pract 14:291–309. https://doi.org/10.1057/dddmp.2013.20

    Article  Google Scholar 

  19. Zhang J, Lei L, Zhang S, Song L (2017) Dynamic vs. static pricing in a supply chain with advertising. Comput Ind Eng 109:266–279. https://doi.org/10.1016/j.cie.2017.05.006

    Article  Google Scholar 

  20. Peters G, Weber R, Nowatzke R (2012) Dynamic rough clustering and its applications. Appl Soft Comput J 12:3193–3207. https://doi.org/10.1016/j.asoc.2012.05.015

    Article  Google Scholar 

  21. Min SH, Han I (2005) Detection of the customer time-variant pattern for improving recommender systems. Expert Syst Appl 28:189–199. https://doi.org/10.1016/j.eswa.2004.10.001

    Article  Google Scholar 

  22. Chen YL, Kuo MH, Wu SY, Tang K (2009) Discovering recency, frequency, and monetary (RFM) sequential patterns from customers’ purchasing data. Electron Commer Res Appl 8:241–251. https://doi.org/10.1016/j.elerap.2009.03.002

    Article  Google Scholar 

  23. Apeh E, Gabrys B (2013) Detecting and Visualizing the Change in Classification of Customer Profiles based on Transactional Data. Evol Syst 4:27–42. https://doi.org/10.1007/s12530-012-9065-2

    Article  Google Scholar 

  24. Lim S, Lee B (2015) Loyalty programs and dynamic consumer preference in online markets. Decis Support Syst 78:104–112. https://doi.org/10.1016/j.dss.2015.05.008

    Article  Google Scholar 

  25. Rust RT, Kumar V, Venkatesan R (2011) Will the frog change into a prince? Predicting future customer profitability. Int J Res Mark 28:281–294. https://doi.org/10.1016/j.ijresmar.2011.05.003

    Article  Google Scholar 

  26. Raghu TS, Kannan PK, Rao HR, Whinston AB (2001) Dynamic profiling of consumers for customized offerings over the Internet: A model and analysis. Decis Support Syst 32:117–134. https://doi.org/10.1016/S0167-9236(01)00106-3

    Article  Google Scholar 

  27. Reutterer T, Mild A, Natter M, Taudes A (2006) A dynamic segmentation approach for targeting and customizing direct marketing campaigns. J Interact Mark. https://doi.org/10.1002/dir.20066

  28. Abualigah LMQ (2019) Feature selection and enhanced krill herd algorithm for text document clustering

  29. Abualigah LM, Khader AT, Hanandeh ES (2018) A new feature selection method to improve the document clustering using particle swarm optimization algorithm. J Comput Sci 25:456–466. https://doi.org/10.1016/j.jocs.2017.07.018

    Article  Google Scholar 

  30. Qasim Abualigah LM, Hanandeh SE (2015) Applying Genetic Algorithms to Information Retrieval Using Vector Space Model. Int J Comput Sci Eng Appl 5:19–28. https://doi.org/10.5121/ijcsea.2015.5102

    Article  Google Scholar 

  31. Abualigah LM, Khader AT (2017) Unsupervised text feature selection technique based on hybrid particle swarm optimization algorithm with genetic operators for the text clustering. J Supercomput 73:4773–4795. https://doi.org/10.1007/s11227-017-2046-2

    Article  Google Scholar 

  32. Abualigah LM, Khader AT, Hanandeh ES (2018) A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis. Eng Appl Artif Intell 73:111–125. https://doi.org/10.1016/j.engappai.2018.05.003

    Article  Google Scholar 

  33. Bose I, Chen X (2015) Detecting the migration of mobile service customers using fuzzy clustering. Inf Manag 52:227–238. https://doi.org/10.1016/j.im.2014.11.001

    Article  Google Scholar 

  34. Abualigah LM, Khader AT, Hanandeh ES (2018) Hybrid clustering analysis using improved krill herd algorithm. Appl Intell 48:4047–4071. https://doi.org/10.1007/s10489-018-1190-6

    Article  Google Scholar 

  35. Kim YA, Song HS, Kim SH (2009) A new marketing strategy map for direct marketing. Knowledge-Based Syst 22:327–335. https://doi.org/10.1016/j.knosys.2009.02.013

    Article  Google Scholar 

  36. Tavakoli M, Molavi M, Masoumi V, et al (2018) Customer Segmentation and Strategy Development Based on User Behavior Analysis, RFM Model and Data Mining Techniques: A Case Study. Proc - 2018 IEEE 15th Int Conf E-bus Eng ICEBE 2018 119–126. 10.1109/ICEBE.2018.00027

  37. Guo Z, Zhou K, Zhang X et al (2018) Data mining based framework for exploring household electricity consumption patterns: A case study in China context. J Clean Prod 195:773–785. https://doi.org/10.1016/j.jclepro.2018.05.254

    Article  Google Scholar 

  38. Chen D, Guo K, Ubakanma G (2015) Predicting customer profitability over time based on RFM time series. Int J Bus Forecast Mark Intell 2:1. https://doi.org/10.1504/ijbfmi.2015.075325

    Article  Google Scholar 

  39. Ramon-Gonen R, Gelbard R (2017) Cluster evolution analysis: Identification and detection of similar clusters and migration patterns. Expert Syst Appl 83:363–378. https://doi.org/10.1016/j.eswa.2017.04.007

    Article  Google Scholar 

  40. Hiziroglu A (2015) Observing Customer Segment Stability Using Soft Computing Techniques and Markov Chains within Data Mining Framework. Int J Inf Syst Soc Chang 6:59–75. https://doi.org/10.4018/ijissc.2015010104

    Article  Google Scholar 

  41. Ha SH (2007) Applying knowledge engineering techniques to customer analysis in the service industry. Adv Eng Inform 21:293–301. https://doi.org/10.1016/j.aei.2006.12.001

    Article  Google Scholar 

  42. Subbalakshmi C, Rama Krishna G, Krishna Mohan Rao S, Venketeswa Rao P (2015) A method to find optimum number of clusters based on fuzzy silhouette on dynamic data set. Procedia Comput Sci 46:346–353. https://doi.org/10.1016/j.procs.2015.02.030

    Article  Google Scholar 

  43. Viegas JL, Vieira SM, Melício R et al (2016) Classification of new electricity customers based on surveys and smart metering data. Energy 107:804–817. https://doi.org/10.1016/j.energy.2016.04.065

    Article  Google Scholar 

  44. Haining T, Juanjuan X, Bian Z (2009) Research on Index System of Dynamic Customer Segmentation : Based on the case study of China telecom. Proc - 2009 2nd IEEE Int Conf Comput Sci Inf Technol ICCSIT 2009 197–201. 10.1109/ICCSIT.2009.5234562

  45. Bezdek JC, Ehrlich R, Full W (1984) FCM: The fuzzy c-means clustering algorithm. Comput Geosci 10:191–203. https://doi.org/10.1016/0098-3004(84)90020-7

    Article  Google Scholar 

  46. Fathabadi H (2016) Power distribution network reconfiguration for power loss minimization using novel dynamic fuzzy c-means (dFCM) clustering based ANN approach. Int J Electr Power Energy Syst 78:96–107. https://doi.org/10.1016/j.ijepes.2015.11.077

    Article  Google Scholar 

  47. Jahangoshai Rezaee M, Jozmaleki M, Valipour M (2018) Integrating dynamic fuzzy C-means, data envelopment analysis and artificial neural network to online prediction performance of companies in stock exchange. Phys A Stat Mech its Appl 489:78–93. https://doi.org/10.1016/j.physa.2017.07.017

    Article  Google Scholar 

  48. Xie XL, Beni G (1991) A validity measure for fuzzy clustering. IEEE Trans Pattern Anal Mach Intell 13:841–847

    Article  Google Scholar 

  49. Alp Erilli N, Yolcu U, Eǧrioǧlu E et al (2011) Determining the most proper number of cluster in fuzzy clustering by using artificial neural networks. Expert Syst Appl 38:2248–2252. https://doi.org/10.1016/j.eswa.2010.08.012

    Article  Google Scholar 

  50. Hosseini M, Shabani M (2015) New approach to customer segmentation based on changes in customer value. J Mark Anal 3:110–121. https://doi.org/10.1057/jma.2015.10

    Article  Google Scholar 

  51. Birant D (2011) Data Mining Using RFM Analysis. Knowledge-Oriented Appl Data Min:91–108. https://doi.org/10.5772/13683

  52. Weng CH, Huang TCK (2018) Observation of sales trends by mining emerging patterns in dynamic markets. Appl Intell 48:4515–4529. https://doi.org/10.1007/s10489-018-1231-1

    Article  Google Scholar 

  53. Wedel M, Kamakura WA (2000) Market Segmentation: Conceptual and Methodological Foundations (International Series in Quantitative Marketing)

  54. Chao HC, Tang KA, Liu YH, Hsu CY (2017) Using kernel density estimation to target customer complaint handling service. 19th Asia-Pacific Netw Oper Manag Symp Manag a World Things, APNOMS 2017 215–218. 10.1109/APNOMS.2017.8094130

  55. Lin QY, Chen YL, Chen JS, Chen YC (2003) Mining inter-organizational retailing knowledge for an alliance formed by competitive firms. Inf Manag 40:431–442. https://doi.org/10.1016/S0378-7206(02)00062-9

    Article  Google Scholar 

  56. Kwon SH (1998) Cluster validity index for fuzzy clustering. Electron Lett 34:2176–2177

    Article  Google Scholar 

  57. Tang Y, Sun F, Sun Z (2005) Improved validation index for fuzzy clustering. In: Proceedings of the 2005, American Control Conference, 2005. IEEE, pp 1120–1125

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Murugesan Punniyamoorthy.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Munusamy, S., Murugesan, P. Modified dynamic fuzzy c-means clustering algorithm – Application in dynamic customer segmentation. Appl Intell 50, 1922–1942 (2020). https://doi.org/10.1007/s10489-019-01626-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10489-019-01626-x

Keywords

Navigation