Seleção de algoritmos para a tarefa de agrupamento de dados: uma abordagem via meta-aprendizagem

dc.contributor.advisorSilva, Leandro Nunes de Castropt_BR
dc.contributor.advisor1Latteshttp://lattes.cnpq.br/2741458816539568por
dc.contributor.authorFerrari, Daniel Gomespt_BR
dc.creator.Latteshttp://lattes.cnpq.br/2650691713057509por
dc.date.accessioned2016-03-15T19:38:50Z
dc.date.accessioned2020-05-28T18:07:56Z
dc.date.available2014-06-30pt_BR
dc.date.available2020-05-28T18:07:56Z
dc.date.issued2014-03-27pt_BR
dc.description.abstractData clustering is an important data mining task that aims to segment a database into groups of objects based on their similarity or dissimilarity. Due to the unsupervised nature of clustering, the search for a good quality solution can become a complex process. There is currently a wide range of clustering algorithms and selecting the most suitable one for a given problem can be a slow and costly process. In 1976, Rice formulated the algorithm selection problem (PSA) postulating that a good performance algorithm can be chosen according to the problem s structural characteristics. Meta-learning brings the concept of learning about learning, that is, the meta-knowledge obtained from the algorithms learning process allows it to improve its performance. Meta-learning has a major intersection with data mining in classification problems, where it is used to select algorithms. This thesis proposes an approach to the algorithm selection problem by using meta-learning techniques for clustering. The characterization of 84 problems is performed by a classical approach, based on the problems, and a new proposal based on the similarity among the objects. Ten internal indices are used to provide different performance assessments of seven algorithms, where the combination of the indices determine the ranking for the algorithms. Several analyzes are performed in order to assess the quality of the obtained meta-knowledge in facilitating the mapping between the problem s features and the performance of the algorithms. The results show that the new characterization approach and method to combine the indices provide a good quality algorithm selection mechanism for data clustering problems.eng
dc.description.sponsorshipNatcomp Informatica e Equipamentos Eletronicos LTDApt_BR
dc.formatapplication/pdfpor
dc.identifier.citationFERRARI, Daniel Gomes. Seleção de algoritmos para a tarefa de agrupamento de dados: uma abordagem via meta-aprendizagem. 2014. 204 f. Tese (Doutorado em Engenharia Elétrica) - Universidade Presbiteriana Mackenzie, São Paulo, 2014.por
dc.identifier.urihttp://dspace.mackenzie.br/handle/10899/24259
dc.languageporpor
dc.publisherUniversidade Presbiteriana Mackenziepor
dc.rightsAcesso Abertopor
dc.subjectagrupamento de dadospor
dc.subjectmeta-aprendizagempor
dc.subjectmeta-conhecimentopor
dc.subjectseleção de algoritmospor
dc.subjectdata clusteringeng
dc.subjectmeta-learningeng
dc.subjectmeta-knowledgeeng
dc.subjectalgorithm selectioneng
dc.subject.cnpqCNPQ::ENGENHARIAS::ENGENHARIA ELETRICApor
dc.thumbnail.urlhttp://tede.mackenzie.br/jspui/retrieve/3809/Daniel%20Gomes%20Ferrari.pdf.jpg*
dc.titleSeleção de algoritmos para a tarefa de agrupamento de dados: uma abordagem via meta-aprendizagempor
dc.typeTesepor
local.contributor.board1Omar, Nizampt_BR
local.contributor.board1Latteshttp://lattes.cnpq.br/2067336430076971por
local.contributor.board2Silva, Leandro Augusto dapt_BR
local.contributor.board2Latteshttp://lattes.cnpq.br/1396385111251741por
local.contributor.board3Carvalho, André Carlos Ponce de Leon Ferreira dept_BR
local.contributor.board3Latteshttp://lattes.cnpq.br/9674541381385819por
local.contributor.board4Medeiros, Claudia Maria Bauzerpt_BR
local.contributor.board4Latteshttp://lattes.cnpq.br/4643608666899616por
local.publisher.countryBRpor
local.publisher.departmentEngenharia Elétricapor
local.publisher.initialsUPMpor
local.publisher.programEngenharia Elétricapor
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