Hybrid Clustering Based on a Graph Model
Hongjun Su, Hong Zhang · 2016
A hybrid clustering approach is proposed for processing image-like data such as plots in flow cytometry. Clustering or partitioning data into relatively homogeneous and coherent subpopulations can be an effective pre-processing method to achieve data analysis tasks such as pattern recognition and classification. Our method uses a graph to model the initial manual partition of the dataset. Based on the graph model, an algorithm is developed for automatic detection of regions defined by the partition. A clustering algorithm using Markov Chain Monte Carlo method is developed for finding optimal adjustments to the partition automatically.