Application of Bio-inspired Metaheuristics in the Data Clustering Problem
Clustering analysis includes a number of different algorithms and methods for grouping objects by their similar characteristics into categories. In recent years, considerable effort has been made to improve such algorithms performance. In this sense, this paper explores three different bio-inspired...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Centro Latinoamericano de Estudios en Informática
2011-12-01
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| Series: | CLEI Electronic Journal |
| Online Access: | http://clei.org/cleiej-beta/index.php/cleiej/article/view/162 |
| _version_ | 1818261548170215424 |
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| author | Thelma Elita Colanzi Wesley Klewerton Guez Assunção Aurora Trinidad Ramirez Pozo Ana Cristina B. Kochem Vendramin Diogo Augusto Barros Pereira Carlos Alberto Zorzo Pedro Luis de Paula Filho |
| author_facet | Thelma Elita Colanzi Wesley Klewerton Guez Assunção Aurora Trinidad Ramirez Pozo Ana Cristina B. Kochem Vendramin Diogo Augusto Barros Pereira Carlos Alberto Zorzo Pedro Luis de Paula Filho |
| author_sort | Thelma Elita Colanzi |
| collection | DOAJ |
| description | Clustering analysis includes a number of different algorithms and methods for grouping objects by their similar characteristics into categories. In recent years, considerable effort has been made to improve such algorithms performance. In this sense, this paper explores three different bio-inspired metaheuristics in the clustering problem: Genetic Algorithms (GAs), Ant Colony Optimization (ACO), and Artificial Immune Systems (AIS). This paper proposes some refinements to be applied to these metaheuristics in order to improve their performance in the data clustering problem. The performance of the proposed algorithms is compared on five different numeric UCI databases. The results show that GA, ACO and AIS based algorithms are able to efficiently and automatically forming natural groups from a pre-defined number of clusters. |
| first_indexed | 2024-12-12T18:48:58Z |
| format | Article |
| id | doaj.art-dc99825541b54c27a75385330438853d |
| institution | Directory Open Access Journal |
| issn | 0717-5000 |
| language | English |
| last_indexed | 2024-12-12T18:48:58Z |
| publishDate | 2011-12-01 |
| publisher | Centro Latinoamericano de Estudios en Informática |
| record_format | Article |
| series | CLEI Electronic Journal |
| spelling | doaj.art-dc99825541b54c27a75385330438853d2022-12-22T00:15:26ZengCentro Latinoamericano de Estudios en InformáticaCLEI Electronic Journal0717-50002011-12-0114310.19153/cleiej.14.3.5Application of Bio-inspired Metaheuristics in the Data Clustering ProblemThelma Elita ColanziWesley Klewerton Guez AssunçãoAurora Trinidad Ramirez PozoAna Cristina B. Kochem VendraminDiogo Augusto Barros PereiraCarlos Alberto ZorzoPedro Luis de Paula FilhoClustering analysis includes a number of different algorithms and methods for grouping objects by their similar characteristics into categories. In recent years, considerable effort has been made to improve such algorithms performance. In this sense, this paper explores three different bio-inspired metaheuristics in the clustering problem: Genetic Algorithms (GAs), Ant Colony Optimization (ACO), and Artificial Immune Systems (AIS). This paper proposes some refinements to be applied to these metaheuristics in order to improve their performance in the data clustering problem. The performance of the proposed algorithms is compared on five different numeric UCI databases. The results show that GA, ACO and AIS based algorithms are able to efficiently and automatically forming natural groups from a pre-defined number of clusters.http://clei.org/cleiej-beta/index.php/cleiej/article/view/162 |
| spellingShingle | Thelma Elita Colanzi Wesley Klewerton Guez Assunção Aurora Trinidad Ramirez Pozo Ana Cristina B. Kochem Vendramin Diogo Augusto Barros Pereira Carlos Alberto Zorzo Pedro Luis de Paula Filho Application of Bio-inspired Metaheuristics in the Data Clustering Problem CLEI Electronic Journal |
| title | Application of Bio-inspired Metaheuristics in the Data Clustering Problem |
| title_full | Application of Bio-inspired Metaheuristics in the Data Clustering Problem |
| title_fullStr | Application of Bio-inspired Metaheuristics in the Data Clustering Problem |
| title_full_unstemmed | Application of Bio-inspired Metaheuristics in the Data Clustering Problem |
| title_short | Application of Bio-inspired Metaheuristics in the Data Clustering Problem |
| title_sort | application of bio inspired metaheuristics in the data clustering problem |
| url | http://clei.org/cleiej-beta/index.php/cleiej/article/view/162 |
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