@inproceedings{6e5756a4848c4fc995f4d464f6fe1da2,
title = "Hybrid Clustering Based on a Graph Model",
abstract = "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.",
keywords = "Markov Chain Monte Carlo method, clustering, image partition, machine learning, planar graph",
author = "Hongjun Su and Hong Zhang",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 9th International Symposium on Computational Intelligence and Design, ISCID 2016 ; Conference date: 10-12-2016 Through 11-12-2016",
year = "2016",
month = jul,
day = "2",
doi = "10.1109/ISCID.2016.1062",
language = "English",
series = "Proceedings - 2016 9th International Symposium on Computational Intelligence and Design, ISCID 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "242--245",
booktitle = "Proceedings - 2016 9th International Symposium on Computational Intelligence and Design, ISCID 2016",
address = "United States",
}