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Comparison of Clustering Methods in Cotton Textile Industry

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9375)

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.

Keywords

Data clustering Number of clusters k-means algorithm Fuzzy c-means Random algorithm 

Notes

Acknowledgments

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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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Faculty of Technical SciencesUniversity of Novi SadNovi SadSerbia
  2. 2.Department of Systems and Computer NetworksWroclaw University of TechnologyWrocławPoland
  3. 3.Faculty of MedicineUniversity of Novi SadNovi SadSerbia

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