An Efficiency K-Means Data Clustering in Cotton Textile Imports

被引：5

作者：

Simic, Dragan ^{[1
]}

Svircevic, Vasa ^{[2
]}

Sremac, Sinisa ^{[1
]}

Ilin, Vladimir ^{[1
]}

Simic, Svetlana ^{[3
]}

机构：

[1] Univ Novi Sad, Fac Tech Sci, Trg Dositeja Obradovica 6, Novi Sad 21000, Serbia

[2] Lames Ltd, Jaracki Put Bb, Sremska Mitrovica 22000, Serbia

[3] Univ Novi Sad, Fac Med, Hajduk Veljkova 1-9, Novi Sad 21000, Serbia

来源：

PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS, CORES 2015 | 2016年 / 403卷

关键词：

Data clustering; Cluster; k-means algorithm; Random algorithm; NUMBER;

D O I：

10.1007/978-3-319-26227-7_24

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Data clustering is a technique of finding similar characteristics among the data sets which are always hidden in nature, and dividing them into groups. The major factor influencing cluster validation is choosing the optimal number of clusters. A novel random algorithm for estimating the optimal number of clusters is introduced here. The efficiency hybrid random algorithm for good k and modified classical k-means data clustering method in cotton textile imports country clustering and ranking is described and implemented on real-world data set. The original real-world U.S. cotton textile and apparel imports data set is taken under view in this research.

引用

页码：255 / 264

页数：10

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