Abstract
Clustering is the task of partitioning data objects into groups, so that the objects within a cluster are similar to one another and dissimilar to the objects in other clusters. The efficiency random algorithm for good k is used to estimate the optimal number of clusters. In this research two important clustering algorithms, namely centroid based k-means, and representative object based fuzzy c-means clustering algorithms are compared in the original real-world U.S. cotton textile and apparel imports data set. This data set is not analyzed very often, it is dictated by business, economics and politics environments and its behaviour is not well known. The analysis of several different real-world economies and industrial data sets of one country is possible to predict it’s economic development.
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The authors acknowledge the support for research project TR 36030, funded by the Ministry of Science and Technological Development of Serbia.
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Simić, D., Jackowski, K., Jankowski, D., Simić, S. (2015). Comparison of Clustering Methods in Cotton Textile Industry. In: Jackowski, K., Burduk, R., Walkowiak, K., Wozniak, M., Yin, H. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2015. IDEAL 2015. Lecture Notes in Computer Science(), vol 9375. Springer, Cham. https://doi.org/10.1007/978-3-319-24834-9_58
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DOI: https://doi.org/10.1007/978-3-319-24834-9_58
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