Name
Abstract | ||
|---|---|---|
Spectral clustering approaches have led to well-accepted algorithms for finding accurate clusters in a given dataset. However, their application to large-scale datasets has been hindered by computational complexity of eigenvalue decompositions. Several algorithms have been proposed in the recent past to accelerate spectral clustering, however they compromise on the accuracy of the spectral clustering to achieve faster speed. In this paper, we propose a novel spectral clustering algorithm based on a mixing process on a graph. Unlike the existing spectral clustering algorithms, our algorithm does not require computing eigenvectors. Specifically, it finds the equivalent of a linear combination of eigenvectors of the normalized similarity matrix weighted with corresponding eigenvalues. This linear combination is then used to partition the dataset into meaningful clusters. Simulations on real datasets show that partitioning datasets based on such linear combinations of eigenvectors achieves better accuracy than standard spectral clustering methods as the number of clusters increase. Our algorithm can easily be implemented in a distributed setting. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/TNSE.2018.2821598 | IEEE Trans. Network Science and Engineering |
Keywords | Field | DocType |
Clustering algorithms,Signal processing algorithms,Partitioning algorithms,Markov processes,Machine learning algorithms,Approximation algorithms,Atmospheric measurements | Spectral clustering,Fuzzy clustering,Canopy clustering algorithm,Mathematical optimization,CURE data clustering algorithm,Data stream clustering,Correlation clustering,Determining the number of clusters in a data set,Artificial intelligence,Cluster analysis,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
abs/1603.04918 | 3 | 2327-4697 |
Citations | PageRank | References |
2 | 0.38 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shahzad Bhatti | 1 | 2 | 0.72 |
| Carolyn L. Beck | 2 | 401 | 60.19 |
| Angelia Nedic | 3 | 2323 | 148.65 |