Comparison of two fuzzy algorithms in geodemographic segmentation analysis: The fuzzy C-means and Gustafson-Kessel methods

Applied Geography

Volumn 34, Issue , 2012, Pages 125-136

  Grekousis, George ^a   Thomas, Hatzichristos ^b  

^a UNIVERSITY OF THESSALY   (Greece)

^b NATIONAL TECHNICAL UNIVERSITY OF ATHENS   (Greece)

Author keywords

Fuzzy C means; Geodemographic segmentation; Gustfson kessel algorithm

Indexed keywords

ALGORITHM; CENSUS; CLUSTER ANALYSIS; DATA SET; EXPERIMENTAL STUDY; FUZZY MATHEMATICS; METROPOLITAN AREA; SEGMENTATION;

ATHENS [ATTICA]; ATTICA; GREECE;

EID: 84155179103     PISSN: 01436228     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.apgeog.2011.11.004     Document Type: Article

References (56)

1. Adnan, M., Longley, P. A., Singleton, A. D., & Brunsdon, C. (2010). Towards real-time geodemographics: clustering algorithm performance for large multidimensional spatial databases. Transactions in GIS, 14, 283-297. doi:10.1111/j.1467-9671.2010.01197.x.
2. Babuska, R. (1998). Fuzzy modelling for control. Boston: Kluwer Academic Publishers.
3. Bacao, F., Lobo, V., & Painho, M. (2005). Applying genetic algorithms to zone design. Soft Computing, 9, 341-348.
4. Balasko B., Abonyi J., and Feil B., 2003. Fuzzy Clustering and Data Analysis Toolbox: For Use with Matlab. Phd Thesis. Department of Process Engineering University of Veszprem, Hungary.
5. Bensaid, A. M., Hall, L. O., Bezdek, J. C., Clarke, L. P., Silbiger, M. L., Arrington, J. A., et al. (1996). Validity-guided (Re)Clustering with applications to image segmentation. IEEE Transactions on Fuzzy Systems, 4, 112-123.
6. Bezdek, J.,C. (1981). Pattern recognition with fuzzy objective function algorithms. Plenum Press.
7. Bishop, C. M. (1995). Neural networks for pattern recognition. Oxford University Press.
8. Bradley, P., Fayyad, U., 1998. Refining initial points for K-means clustering. In International Conference on Machine Learning (ICML-98).
9. Brimicombe, A. J. (2007). A dual approach to cluster discovery in point event data sets. Computers, Environment and Urban Systems, 31, 4-18.
10. Brunsdon, C. (2006). A cluster-based approach to the zoning problem using an extended genetic algorithm. GIS research. UK, Nottingham: University of Nottingham.
11. Boyer, L., & Burgaud, D. (2000). Le marketing avance. Du one to one au e-business. Paris: Editions d' Organisation.
12. CACI. (2005). Acorn: The smarter consumer classification user guide. London: CACI. http://downloads.postcodeanywhere.co.uk/pdf/acorn_user_guide.pdf Accessed 25.10.11.
13. Dunn, J. C. (1973). A fuzzy relative of the ISODATA process and its use in detecting compact well-separated clusters. Journal of Cybernetics, 3, 32-57.
14. ESRI. (2010). Methodology statement: Tapestry segmentation for block groups and ZIP+4s. White paper ESRI. http://www.esri.com/library/whitepapers/pdfs/esridata-tapestry-segmentation.pdf Accessed 26.10.11.
15. Experian. (2010). Optimise the value of your customers and locations, now and in the future. London: Experian. http://www.experian.co.uk/assets/business-strategies/brochures/mosaic-uk-2009-brochure-jun10.pdf Accessed 28.10.11.
16. Feng, Z., & Flowerdew, R. (1998). Fuzzy geodemographics: a contribution from fuzzy clustering method. In S. Carver (Ed.), Innovations in GIS 5 (pp. 119-127). London: Taylor and Francis.
17. Feng, Z., & Flowerdew, R. (1999). The use of fuzzy classification to improve geodemographic targeting. In B. Gittings (Ed.), Innovations in GIS 6 (pp. 133-144). London: Taylor &Francis.
18. Fleischmann, B. J., & Paraschis, N. (1988). Solving a large scale districting problem: a case report. Computers and Operations Research, 15, 521-533.
19. Fritz, L., See, l., & Carver, S. (2000). A fuzzy modelling approach to wild land mapping in Scotland, GIS and geocomputation. Taylor & Francis.
20. Gerla, G. (2006). Effectiveness and multivalued logics. Journal of Symbolic Logic, 71(1), 137-162.
21. Gibin, M., Singleton, A., Milton, R., Mateos, P., & Longley, P. (2008). An exploratory cartographic visualisation of London through the Google maps API. Applied Spatial Analysis and Policy, 1(2), 85-97. doi:10.1007/s12061-008-9005-5.
22. Grekousis, G., & Fotis, Y. (2011). A fuzzy index for detecting spatiotemporal outliers. GeoInformatica,. doi:10.1007/s10707-011-0145-4.
23. Gustafson, D.E., and Kessel, W.C., 1979. Fuzzy clustering with fuzzy covariance matrix. In Proceedings of the IEEE CDC, San Diego, pp 761-766.
24. Hart, J. F. (1982). The highest form of the geographer's art. Annals of the Association of American Geographers1-29, No 72.
25. Hatzichristos, T., & Koutsopoulos, K., 1998. Delineation of Demographic Regions in Athens Municipality using Fuzzy Classification. InGISPlanet98Conference, Lisbon,Portugal.
26. Hatzichristos, T. (2004). Delineation of demographic regions with GIS and computational intelligence. Environment and Planning B, 31, 39-49.
27. Hakley, M., Singleton, A., & Park, C. (2008). Web mapping 2.0: the neogeography of the GeoWeb. Geography Compass, 2(6), 2011-2039.
28. Harris, R., Sleight, P., & Webber, R. (2005). Geodemographics, GIS and neighbourhood targeting. London: Wiley.
29. Jennings, R., & Dubitsky, T. (2003). How to prospect for customers using ZIP code profile models. Quirk's Marketing Research Review. http://www.quirks.com/articles/a2003/20030110.aspx?searchID=25461237&sort=9 Article ID 20030110Accessed 12.11.08.
30. Kohonen, T. (1982). Self organised formation of topologically correct feature maps. Biological Cybernetics59-69, No 43.
31. Kohonen, T. (1988). Self organisation and associative memory. Berlin: Springer.
32. Kohonen, T. (2001). Self-organizing maps. Information sciences. Springer.
33. MacQueen, J. B. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of 5th Berkeley symposium on mathematical statistics and probability (pp. 281-297). University of California Press.
34. Martin, D. (1998). From enumeration districts to output areas: experiments in the automated creation of a census output geography. Population Trends, 88, 36-42.
35. Martin, D. (2001). Census output areas: from concept to prototype. Population Trends, 94, 19-24.
36. Michie, D., Spiegelhalter, D. J., & Taylor, C. C. (1994). Machine learning, neural and statistical classification. Oxford: Oxford University Press.
37. Morphet,C. (1993).Themappingof small-areacensusdataea considerationof the effects of enumeration district boundaries. Environment and Planning A, 25, 1267-1277.
38. Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. Computer Journal, 7, 308-313.
39. Openshaw, S., & Gillard, A. (1978). On the stability of a spatial classification of census enumaration districts data. In P. W. S. Batey (Ed.), Theory and methods in urban and regional analysis (pp. 101-119). London: Pion.
40. Openshaw, S., & Wymer, C. (1991). A neural net classifier system for handling census data. In F. Murtagh (Ed.), Proceedings of the neural networks for statistical and economic data conference (pp. 73-86). Dublin: Munotec Systems.
41. Openshaw, S. (1991). In I. Masser, & M. Blakemore (Eds.), A spatial analysis research agenda in handling geographical information. London: Longman.
42. Openshaw, S., & Wymer, C. (1994). Classifying and regionalizing census data. In Census users handbook (pp. 239-270). Cambridge, UK: Geo Information International.
43. Openshaw, S. (1995). Census users handbook. Geoinformation International.
44. Openshaw, S. (1997). Artificial intelligence in geography. London: Wiley.
45. Petersen, J., Gibin, M., Longley, P., Mateos, P., Atkinson, P., & Ashby, D. (2011). Geodemographics as a tool for targeting neighbourhoods in public health campaigns. Journal of Geographical Systems, 10.1007 10109-010-0113-9.
46. Sammon, J. W. (1969). A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, 18, 401-409.
47. See, L., & Openshaw, S. (2001). Fuzzy geodemographics targeting. Regional science in business. Berlin: Springer.
48. Singleton, A., & Longley, P. (2009). Geodemographics, visualisation, and social networks in applied geography. Applied Geography, 29, 289-298.
49. Singleton, A., & Longley, P. (2008). Creating open source geodemographicserefining a national classification of census output areas for applications in higher education. Papers in Regional Science, 88(3), 643-666.
50. Sleight, P. (1997). Targeting customers: How to use geodemographic and lifestyle data in your business. Henley-on-Thames: NTC Publications.
51. Spielman, S. E., & Thill, J. C. (2008). Social area analysis, data mining and GIS. Computers, Environment and Urban Systems, 32, 110-122.
52. Stockburger, D. W. (1996). Multivariate statistics: Concepts, models and applications, applications in geography. University of London.
53. Vickers, D., & Rees, P. (2007). Creating the UK national statistics 2001 output area classification. Journal of the Royal Statistical Society Statistics in Society A, 170, 379.
54. Walt Van C.M., & Barnard, E., 2006. Data characteristics that determine classifier performance. In Proceedings of the Sixteenth Annual Symposium of the Pattern Recognition Association of South Africa, pp.160-165.
55. Xie, X. L., & Beni, G. A. (1991). Validity measure for fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence, 3(8), 841-846.
56. Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8, 338-353.