Hierarchical density-based clustering in high-dimensional spaces using topographic maps

2002 ◽  
Author(s):  
T. Gautama ◽  
M.M. Van Hulle
Keyword(s):  
Topographic Maps ◽  
High Dimensional ◽  
Density Based Clustering

scholarly journals A Novel Radar HRRP Recognition Method with Accelerated T-Distributed Stochastic Neighbor Embedding and Density-Based Clustering

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5112 ◽  
Author(s):  
Hao Wu ◽  
Dahai Dai ◽  
Xuesong Wang
Keyword(s):  
Prior Information ◽  
Signal To Noise Ratio ◽  
Recognition Performance ◽  
Principal Component ◽  
High Dimensional ◽  
Recognition Method ◽  
Range Profile ◽  
Density Based Clustering ◽  
Computation Speed ◽  
High Resolution Range Profile

High-resolution range profile (HRRP) has attracted intensive attention from radar community because it is easy to acquire and analyze. However, most of the conventional algorithms require the prior information of targets, and they cannot process a large number of samples in real time. In this paper, a novel HRRP recognition method is proposed to classify unlabeled samples automatically where the number of categories is unknown. Firstly, with the preprocessing of HRRPs, we adopt principal component analysis (PCA) for dimensionality reduction of data. Afterwards, t-distributed stochastic neighbor embedding (t-SNE) with Barnes–Hut approximation is conducted for the visualization of high-dimensional data. It proves to reduce the dimensionality, which has significantly improved the computation speed. Finally, it is exhibited that the recognition performance with density-based clustering is superior to conventional algorithms under the condition of large azimuth angle ranges and low signal-to-noise ratio (SNR).

scholarly journals Smooth Splicing: A Robust SNN-Based Method for Clustering High-Dimensional Data

2013 ◽  
Vol 2013 ◽  
pp. 1-9 ◽  
Author(s):  
JingDong Tan ◽  
RuJing Wang
Keyword(s):  
Graph Theory ◽  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Clustering Methods ◽  
Single Edge ◽  
Density Based Clustering ◽  
Measure Of Similarity

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.

Sign in / Sign up

Export Citation Format

Share Document