In this section
@article{Liu:2020:10.1007/s41109-019-0248-7,
author = {Liu, Z and Barahona, M},
doi = {10.1007/s41109-019-0248-7},
journal = {Applied Network Science},
pages = {1--20},
title = {Graph-based data clustering via multiscale community detection},
url = {http://dx.doi.org/10.1007/s41109-019-0248-7},
volume = {5},
year = {2020}
}
TY - JOUR
AB - We present a graph-theoretical approach to data clustering, which combines the creation of a graph from the data with Markov Stability, a multiscale community detection framework. We show how the multiscale capabilities of the method allow the estimation of the number of clusters, as well as alleviating the sensitivity to the parameters in graph construction. We use both synthetic and benchmark real datasets to compare and evaluate several graph construction methods and clustering algorithms, and show that multiscale graph-based clustering achieves improved performance compared to popular clustering methods without the need to set externally the number of clusters.
AU - Liu,Z
AU - Barahona,M
DO - 10.1007/s41109-019-0248-7
EP - 20
PY - 2020///
SN - 2364-8228
SP - 1
TI - Graph-based data clustering via multiscale community detection
T2 - Applied Network Science
UR - http://dx.doi.org/10.1007/s41109-019-0248-7
UR - http://arxiv.org/abs/1909.04491v1
UR - https://appliednetsci.springeropen.com/articles/10.1007/s41109-019-0248-7
UR - http://hdl.handle.net/10044/1/75730
VL - 5
ER -