A comparison of clustering algorithms applied to color image quantization
Pattern Recognition Letters - special issue on pattern recognition in practice V
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Clustering by Scale-Space Filtering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift, Mode Seeking, and Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
MRF Clustering for segmentation of color images
Pattern Recognition Letters
Cluster ensembles --- a knowledge reuse framework for combining multiple partitions
The Journal of Machine Learning Research
Learning the Kernel Matrix with Semidefinite Programming
The Journal of Machine Learning Research
Fast nonparametric clustering with Gaussian blurring mean-shift
ICML '06 Proceedings of the 23rd international conference on Machine learning
Image segmentation based on merging of sub-optimal segmentations
Pattern Recognition Letters
Harmony K-means algorithm for document clustering
Data Mining and Knowledge Discovery
The estimation of the gradient of a density function, with applications in pattern recognition
IEEE Transactions on Information Theory
| Hi-index | 0.10 |
Similarity is the core problem of clustering. Clustering algorithms that are based on a certain, fixed type of similarity are not sufficient to explore complicated structures. In this paper, a constructing method for multiple similarity is proposed to deal with complicated structures of data sets. Multiple similarity derives from the local modification of the initial similarity, based on the feedback information of elementary clusters. Combined with the proposed algorithm, the repeated modifications of local similarity measurement generate a hierarchical clustering result. Some synthetic and real data sets are employed to exhibit the superiority of the new clustering algorithm.