@InProceedings{MonteroFalc:2018:DiClAp,
author = "Montero, Ad{\'a}n Echemend{\'{\i}}a and Falc{\~a}o, Alexandre
Xavier",
affiliation = "Laboratory of Image Data Science, Institute of Computing,
University of Campinas and Laboratory of Image Data Science,
Institute of Computing, University of Campinas",
title = "A Divide-and-Conquer Clustering Approach based on Optimum-Path
Forest",
booktitle = "Proceedings...",
year = "2018",
editor = "Ross, Arun and Gastal, Eduardo S. L. and Jorge, Joaquim A. and
Queiroz, Ricardo L. de and Minetto, Rodrigo and Sarkar, Sudeep and
Papa, Jo{\~a}o Paulo and Oliveira, Manuel M. and Arbel{\'a}ez,
Pablo and Mery, Domingo and Oliveira, Maria Cristina Ferreira de
and Spina, Thiago Vallin and Mendes, Caroline Mazetto and Costa,
Henrique S{\'e}rgio Gutierrez and Mejail, Marta Estela and Geus,
Klaus de and Scheer, Sergio",
organization = "Conference on Graphics, Patterns and Images, 31. (SIBGRAPI)",
publisher = "IEEE Computer Society",
address = "Los Alamitos",
keywords = "clustering, optimum-path forest, image segmentation, image
foresting transform, divide-and-conquer.",
abstract = "Data clustering is one of the main challenges when solving Data
Science problems. Despite its progress over almost one century of
research, clustering algorithms still fail in identifying groups
naturally related to the semantics of the problem. Moreover, the
technological advances add crucial challenges with a considerable
data increase, which are not handled by most techniques. We
address these issues by proposing a divide-and-conquer approach to
a clustering technique, which is unique in finding one group per
dome of the probability density function of the data --- the
Optimum-Path Forest (OPF) clustering algorithm. Our approach can
use all samples, or at least many samples, in the unsupervised
learning process without affecting the grouping performance and,
therefore, being less likely to lose relevant grouping
information. We show that it can obtain satisfactory results when
segmenting natural images into superpixels.",
conference-location = "Foz do Igua{\c{c}}u, PR, Brazil",
conference-year = "29 Oct.-1 Nov. 2018",
doi = "10.1109/SIBGRAPI.2018.00060",
url = "http://dx.doi.org/10.1109/SIBGRAPI.2018.00060",
language = "en",
ibi = "8JMKD3MGPAW/3RMKREB",
url = "http://urlib.net/ibi/8JMKD3MGPAW/3RMKREB",
targetfile = "34.pdf",
urlaccessdate = "2024, May 02"
}