Next Article in Journal
From Enumerating to Generating: A Linear Time Algorithm for Generating 2D Lattice Paths with a Given Number of Turns
Previous Article in Journal
Multiobjective Cloud Particle Optimization Algorithm Based on Decomposition
Article Menu

Export Article

Open AccessArticle
Algorithms 2015, 8(2), 177-189; doi:10.3390/a8020177

An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors

1
School of Information Science and Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China
2
School of Electronics and Information Engineering, Taiyuan University of Science and Technology, Taiyuan 030024, China
*
Author to whom correspondence should be addressed.
Academic Editor: Javier Del Ser Lorente
Received: 19 March 2015 / Revised: 27 April 2015 / Accepted: 28 April 2015 / Published: 7 May 2015
View Full-Text   |   Download PDF [286 KB, uploaded 7 May 2015]   |  

Abstract

The construction of a similarity matrix is one significant step for the spectral clustering algorithm; while the Gaussian kernel function is one of the most common measures for constructing the similarity matrix. However, with a fixed scaling parameter, the similarity between two data points is not adaptive and appropriate for multi-scale datasets. In this paper, through quantitating the value of the importance for each vertex of the similarity graph, the Gaussian kernel function is scaled, and an adaptive Gaussian kernel similarity measure is proposed. Then, an adaptive spectral clustering algorithm is gotten based on the importance of shared nearest neighbors. The idea is that the greater the importance of the shared neighbors between two vertexes, the more possible it is that these two vertexes belong to the same cluster; and the importance value of the shared neighbors is obtained with an iterative method, which considers both the local structural information and the distance similarity information, so as to improve the algorithm’s performance. Experimental results on different datasets show that our spectral clustering algorithm outperforms the other spectral clustering algorithms, such as the self-tuning spectral clustering and the adaptive spectral clustering based on shared nearest neighbors in clustering accuracy on most datasets. View Full-Text
Keywords: spectral clustering; similarity measures; Gaussian kernel function; importance of nearest neighbors spectral clustering; similarity measures; Gaussian kernel function; importance of nearest neighbors
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

He, X.; Zhang, S.; Liu, Y. An Adaptive Spectral Clustering Algorithm Based on the Importance of Shared Nearest Neighbors. Algorithms 2015, 8, 177-189.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Algorithms EISSN 1999-4893 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top