The stability and control of discrete processes
The stability and control of discrete processes
Algorithms for clustering data
Algorithms for clustering data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
ACM Computing Surveys (CSUR)
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
ROCK: A Robust Clustering Algorithm for Categorical Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Extremal Eigenvalues of Real Symmetric Matrices with Entries in an Interval
SIAM Journal on Matrix Analysis and Applications
A tutorial on spectral clustering
Statistics and Computing
Improving density-based methods for hierarchical clustering of web pages
Data & Knowledge Engineering
Complex Network Community Detection Based on Swarm Aggregation
ICNC '08 Proceedings of the 2008 Fourth International Conference on Natural Computation - Volume 07
Brief paper: Reaching a consensus via pinning control
Automatica (Journal of IFAC)
Information theoretic measures for clusterings comparison: is a correction for chance necessary?
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Consensus of multiagent systems and synchronization of complex networks: a unified viewpoint
IEEE Transactions on Circuits and Systems Part I: Regular Papers
A time-efficient pattern reduction algorithm for k-means clustering
Information Sciences: an International Journal
Handwritten Data Clustering Using Agents Competition in Networks
Journal of Mathematical Imaging and Vision
| Hi-index | 0.01 |
Recently, many network-based methods have been developed and successfully applied to cluster data. Once the underlying network has been constructed, a clustering method can be applied over its vertices and edges. In this paper, the concept of pinning control in complex networks is applied to cluster data. Firstly, an adaptive method for constructing sparse and connected networks is proposed. Secondly, a dissimilarity measure is computed via a dynamic system in which vertices are expected to reach a consensus state regarding a reference trajectory. The reference is forced into the system by pinning control. A theoretical analysis was carried out to prove the convergence of the dynamic system under certain parameter constraints. The results using real data sets have showed that the proposed method performs well in the presence of clusters with different sizes and shapes comparing to some well-known clustering methods.