Data Density based Clustering with Automated Radii
Hyde, R., & Angelov, P. (2014, December). A fully autonomous Data Density based Clustering technique. In Evolving and Autonomous Learning Systems (EALS), 2014 IEEE Symposium on (pp. 116-123). IEEE. DOI: 10.1109/EALS.2014.7009512 Downloadable from: http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7009512
Hyde, R.; Angelov, P., "Data density based clustering," Computational Intelligence (UKCI), 2014 14th UK Workshop on , vol., no., pp.1,7, 8-10 Sept. 2014 doi: 10.1109/UKCI.2014.6930157 Downloadable from: http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6930157&isnumber=6930143
It should be noted that this work represents an initial exploration into automating the radii estimation for DDC and so craeting the first paramaterless clustering algorithm. This allows custering of data sets with no a priori knowledge of the data.
Files:
DDC_ver01_1.m: Matlab function for the DDC algorithm, see also https://rhyde67.github.io/DDC/
DDCAR_ver02.m: Matlab function for automating the initial radii for use with the DDC algorithm.
csv files: test data with varying number of data samples per cluster.