@InProceedings{LorenaQuiLorCarCes:2019:NeInLi,
author = "Lorena, Luiz Henrique Nogueira and Quiles, Marcos Gon{\c{c}}alves
and Lorena, Luiz Antonio Nogueira and Carvalho, Andr{\'e} C. P.
L. F. de and Cespedes, Juliana Garcia",
affiliation = "{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and
{Universidade Federal de S{\~a}o Paulo (UNIFESP)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)} and {Universidade de
S{\~a}o Paulo (USP)} and {Universidade Federal de S{\~a}o Paulo
(UNIFESP)}",
title = "Qualitative data clustering: a new Integer Linear Programming
model",
year = "2019",
organization = "The International Joint Conference on Neural Networks",
publisher = "IEEE",
abstract = "Qualitative data clustering is a fundamental data analysis task,
with applications in many areas, like medicine, sociology, and
economics. An appealing way to deal with this task is via Integer
Linear Programming, as it avoids inappropriate inferences by the
final user. This approach has two main advantages: the data are
directly used, without the need of being converted to quantitative
values, and the optimal number of clusters is automatically
obtained by solving the optimization problem. However, it might
create large and redundant models, which can limit the size of the
problems it can be applied. Recently, models that are more compact
and able to avoid some redundancy have been proposed in the
literature. These models consume less memory and are faster to
obtain the optimal solution set. In this study, a new model is
introduced and compared with the state-of-the-art alternatives
using datasets from different application domains. Empirical
results show that the new model outperforms its predecessors,
achieving the optimal solution set with lower computational time
and memory consumption.",
conference-location = "Budapest, Hungary",
conference-year = "14-19 July",
isbn = "978-172811985-4",
language = "en",
urlaccessdate = "27 abr. 2024"
}