Algorithms for clustering data
Algorithms for clustering data
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
Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery, and Soft Granular Computing
Data Mining Methods and Models
Data Mining Methods and Models
Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Fast nonparametric clustering with Gaussian blurring mean-shift
ICML '06 Proceedings of the 23rd international conference on Machine learning
Cluster Analysis
Probability density estimation from optimally condensed data samples
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical kernel estimators for data analysis and exploration tasks: theory and applications
MATH'09 Proceedings of the 14th WSEAS International Conference on Applied mathematics
A complete gradient clustering algorithm
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part III
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The aim of this paper is to provide a gradient clustering algorithm in its complete form, suitable for direct use without requiring a deeper statistical knowledge. The values of all parameters are effectively calculated using optimizing procedures. Moreover, an illustrative analysis of the meaning of particular parameters is shown, followed by the effects resulting from possible modifications with respect to their primarily assigned optimal values. The proposed algorithm does not demand strict assumptions regarding the desired number of clusters, which allows the obtained number to be better suited to a real data structure. Moreover, a feature specific to it is the possibility to influence the proportion between the number of clusters in areas where data elements are dense as opposed to their sparse regions. Finally, the algorithm-by the detection of oneelement clusters-allows identifying atypical elements, which enables their elimination or possible designation to bigger clusters, thus increasing the homogeneity of the data set.