Location- and Density-Based Hierarchical Clustering Using Similarity Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Maximum Variance Cluster Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Similarity-Based Robust Clustering Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian mixture density modeling, decomposition, and applications
IEEE Transactions on Image Processing
Cluster number selection for a small set of samples using the Bayesian Ying-Yang model
IEEE Transactions on Neural Networks
| Hi-index | 0.11 |
To overcome the limitation of requiring the cluster threshold with the parametric approach, this paper presents a clustering constraint which first considers an estimate of the global distribution. The clustering process moves from local clusters identifying the data globally to larger clusters with a specified density function. Merging then occurs to provide a statistically supported representation of the data. A hashing-based sequential clustering algorithm is introduced which utilizes the initial and merging constraints. Experimental data shows the methods effectiveness at classifying varying cluster shapes and sizes when compared to recent clustering techniques.