Abstract
Economies are characterized by constant change. This change has several facets ranging from long term effects like economic cycles and short term financial distortion caused by rumors. It also includes socio-economic technological trends or seasonal alteration and many others. “The only constant is change”, the famous saying often credited to the Greek philosopher Heraclitus, summarizes the challenging environment organizations are confronted with. Hence, for any organization, like companies, government agencies or also small family enterprises, one of the main challenges is to discover economic and technological changes as early as possible to smoothly adapt to upcoming new trends or seasonal oscillation. To successfully deal with changing environments dynamic approaches to data mining have gained increasing importance in the last decades. Areas of application range from engineering and management to science and others. In this chapter we introduce to dynamic rough k-means clustering and discuss a real life application in the retail sector.
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Acknowledgements
Support from the Chilean Instituto Sistemas Complejos de Ingeniera (ICM: P-05-004-F, CONICYT: FBO16) is greatly acknowledged (www.isci.cl).
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Crespo, F., Peters, G., Weber, R. (2012). Rough Clustering Approaches for Dynamic Environments. In: Peters, G., Lingras, P., Ślęzak, D., Yao, Y. (eds) Rough Sets: Selected Methods and Applications in Management and Engineering. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-4471-2760-4_3
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