Submitted by Automa??o e Estat?stica (sst@bczm.ufrn.br) on 2017-02-13T19:22:38Z
No. of bitstreams: 1
LeandroRochinkCosta_DISSERT.pdf: 1175831 bytes, checksum: 3fb21392f141799634d3dbee38317729 (MD5) / Approved for entry into archive by Arlan Eloi Leite Silva (eloihistoriador@yahoo.com.br) on 2017-02-14T18:08:27Z (GMT) No. of bitstreams: 1
LeandroRochinkCosta_DISSERT.pdf: 1175831 bytes, checksum: 3fb21392f141799634d3dbee38317729 (MD5) / Made available in DSpace on 2017-02-14T18:08:27Z (GMT). No. of bitstreams: 1
LeandroRochinkCosta_DISSERT.pdf: 1175831 bytes, checksum: 3fb21392f141799634d3dbee38317729 (MD5)
Previous issue date: 2016-08-22 / Ap?s v?rios avan?os na tecnologia de capta??o e armazenamento de dados e do crescimento
de aplica??es que prov?m novas informa??es, o n?mero de elementos informacionais
dispon?veis ? enorme tanto em volume quanto em variedade. Com esse aumento
na quantidade de informa??es, a necessidade de entend?-los e resumi-los se tornou cada
vez mais urgente. O Agrupamento Balanceado de Dados, do ingl?s Balanced Clustering,
visa encontrar grupos de entidades similares que possuam aproximadamente o mesmo tamanho.
Neste trabalho, ? proposta uma nova abordagem heur?stica baseada na metaheur?stica
Busca em Vizinhan?a Vari?vel, do ingl?s Variable Neighborhood Search (VNS),
e na metodologia Menos ? mais, do ingl?s Less is more approach, para o problema de
agrupamento de dados usando o crit?rio da soma m?nima das dist?ncias quadr?ticas com
restri??o de balanceamento dos grupos. Os algoritmos encontrados na literatura n?o s?o
escal?veis ao passo que aumentamos o tamanho do problema para al?m de 5000 elementos
de acordo com experimentos realizados nesta pesquisa. Os experimentos computacionais
mostram que o m?todo proposto supera o atual estado da arte neste problema. / After advances in collecting and storing data and the growth in applications that provide
new information, the number of data elements available is huge in both volume and
variety. With this increase in the quantity of information, the need to understand them and
summarize them has become increasingly urgent. The Balanced Clustering seeks to find
groups of similar entities that have approximately the same size. In this dissertation, we
propose a new heuristic approach based on metaheuristic Variable Neighborhood Search
(VNS) and methodology "Less is More Approach"(LIMA) to data clustering problem
using the criterion of the minimum sum-of-squared distances applying balancing restriction
for the groups. The algorithms found in the literature are not scalable, while the
problem of increased size in addition to elements 5000 in accordance with experiments
performed in this study. The computational experiments show that the proposed method
outperforms the current state of the art for the problem.
| Identifer | oai:union.ndltd.org:IBICT/oai:repositorio.ufrn.br:123456789/21976 |
| Date | 22 August 2016 |
| Creators | Costa, Leandro Rochink |
| Contributors | 03553729406, Martins, Allan de Medeiros, 01979076448, Aloise, D?rio Jos?, 05763088468, Aloise, Daniel |
| Publisher | PROGRAMA DE P?S-GRADUA??O EM ENGENHARIA EL?TRICA E DE COMPUTA??O, UFRN, Brasil |
| Source Sets | IBICT Brazilian ETDs |
| Language | Portuguese |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Source | reponame:Repositório Institucional da UFRN, instname:Universidade Federal do Rio Grande do Norte, instacron:UFRN |
| Rights | info:eu-repo/semantics/openAccess |
Page generated in 0.0072 seconds