Paper Info
Title
A Spectral Clustering Algorithm Based on Hierarchical Method.
Abstract
Most of the clustering algorithms were designed to cluster the data in convex spherical sample space, but their ability was poor for clustering more complex structures. In the past few years, several spectral clustering algorithms were proposed to cluster arbitrarily shaped data in various real applications including image processing and web analysis. However, most of these algorithms were based on k-means, which is a randomized algorithm and makes the algorithm easy to fall into local optimal solutions. Hierarchical method could handle the local optimum well because it organizes data into different groups at different levels. In this paper, we propose a novel clustering algorithm called spectral clustering algorithm based on hierarchical clustering (SCHC), which combines the advantages of hierarchical clustering and spectral clustering algorithms to avoid the local optimum issues. The experiments on both synthetic data sets and real data sets show that SCHC outperforms other six popular clustering algorithms. The method is simple but is shown to be efficient in clustering both convex shaped data and arbitrarily shaped data.
Year
DOI
Venue
2013
10.1007/978-3-642-55192-5_9
AGENTS AND DATA MINING INTERACTION (ADMI 2013)
Keywords
Field
DocType
Data mining,Clustering,Spectral clustering,Hierarchical clustering
Data mining,Fuzzy clustering,CURE data clustering algorithm,Computer science,Artificial intelligence,Cluster analysis,Single-linkage clustering,Canopy clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Constrained clustering,Machine learning
Conference
Volume
ISSN
Citations 
8316
0302-9743
0
PageRank 
References 
Authors
0.34
14
7
Name
Order
Citations
PageRank
Xiwei Chen172.20
Li Liu220.73
Dashi Luo361.19
Guandong Xu464075.03
Yonggang Lu517413.47
Ming Liu613513.12
Rongmin Gao750.75