Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-26227-7_24 | PROCEEDINGS OF THE 9TH INTERNATIONAL CONFERENCE ON COMPUTER RECOGNITION SYSTEMS, CORES 2015 |
Keywords | Field | DocType |
Data clustering,Cluster,k-means algorithm,Random algorithm | k-means clustering,Cluster (physics),Randomized algorithm,Data set,Division (mathematics),Pattern recognition,Ranking,Computer science,Textile,Artificial intelligence,Cluster analysis | Conference |
Volume | ISSN | Citations |
403 | 2194-5357 | 1 |
PageRank | References | Authors |
0.41 | 1 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dragan Simic | 1 | 40 | 12.78 |
| Vasa Svircevic | 2 | 27 | 5.65 |
| Siniša Sremac | 3 | 14 | 3.92 |
| Vladimir Ilin | 4 | 3 | 2.52 |
| Svetlana Simic | 5 | 40 | 12.78 |