Smooth Splicing: A Robust SNN-Based Method for Clustering High-Dimensional Data
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-06-06
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Sharing nearest neighbor (SNN) is a novel metric measure of similarity, and it can conquer two hardships: the low similarities between samples and the different densities of classes.
At present, there are two popular SNN similarity based clustering methods: JP clustering and SNN density based clustering.
Their clustering results highly rely on the weighting value of the single edge, and thus they are very vulnerable.
Motivated by the idea of smooth splicing in computing geometry, the authors design a novel SNN similarity based clustering algorithm within the structure of graph theory.
Since it inherits complementary intensity-smoothness principle, its generalizing ability surpasses those of the previously mentioned two methods.
The experiments on text datasets show its effectiveness.
American Psychological Association (APA)
Tan, JingDong& Wang, RuJing. 2013. Smooth Splicing: A Robust SNN-Based Method for Clustering High-Dimensional Data. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1008962
Modern Language Association (MLA)
Tan, JingDong& Wang, RuJing. Smooth Splicing: A Robust SNN-Based Method for Clustering High-Dimensional Data. Mathematical Problems in Engineering No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1008962
American Medical Association (AMA)
Tan, JingDong& Wang, RuJing. Smooth Splicing: A Robust SNN-Based Method for Clustering High-Dimensional Data. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1008962
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1008962