Please use this identifier to cite or link to this item:
https://open.uns.ac.rs/handle/123456789/5446
Title: | Comparison of clustering methods in cotton textile industry | Authors: | Simić, Dragan Jackowski K. Jankowski D. Simić, Maja |
Issue Date: | 1-Jan-2015 | Journal: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Abstract: | © Springer International Publishing Switzerland 2015. 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. | URI: | https://open.uns.ac.rs/handle/123456789/5446 | ISSN: | 3029743 | DOI: | 10.1007/978-3-319-24834-9_58 |
Appears in Collections: | FTN Publikacije/Publications |
Show full item record
SCOPUSTM
Citations
3
checked on May 3, 2024
Page view(s)
28
Last Week
8
8
Last month
4
4
checked on May 10, 2024
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.