Clustering algorithm selection by meta-learning systems: A new distance-based problem characterization and ranking combination methods

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dc.contributor.authorFerrari D.G.
dc.contributor.authorDe Castro L.N.
dc.date.accessioned2024-03-13T00:57:21Z
dc.date.available2024-03-13T00:57:21Z
dc.date.issued2015
dc.description.abstract© 2015 Elsevier Inc.Data clustering aims to segment a database into groups of objects based on the similarity among these objects. Due to its unsupervised nature, the search for a good-quality solution can become a complex process. There is currently a wide range of clustering algorithms, and selecting the best one for a given problem can be a slow and costly process. In 1976, Rice formulated the Algorithm Selection Problem (ASP), which postulates that the algorithm performance can be predicted based on the structural characteristics of the problem. Meta-learning brings the concept of learning about learning; that is, the meta-knowledge obtained from the algorithm learning process allows the improvement of the algorithm performance. Meta-learning has a major intersection with data mining in classification problems, in which it is normally used to recommend algorithms. The present paper proposes new ways to obtain meta-knowledge for clustering tasks. Specifically, two contributions are explored here: (1) a new approach to characterize clustering problems based on the similarity among objects; and (2) new methods to combine internal indices for ranking algorithms based on their performance on the problems. Experiments were conducted to evaluate the recommendation quality. The results show that the new meta-knowledge provides high-quality algorithm selection for clustering tasks.
dc.description.firstpage181
dc.description.lastpage194
dc.description.volume301
dc.identifier.doi10.1016/j.ins.2014.12.044
dc.identifier.issn0020-0255
dc.identifier.urihttps://dspace.mackenzie.br/handle/10899/36206
dc.relation.ispartofInformation Sciences
dc.rightsAcesso Restrito
dc.subject.otherlanguageAlgorithm ranking
dc.subject.otherlanguageAlgorithm selection
dc.subject.otherlanguageClustering
dc.subject.otherlanguageMeta-knowledge
dc.subject.otherlanguageMeta-learning systems
dc.subject.otherlanguageProblem characterization
dc.titleClustering algorithm selection by meta-learning systems: A new distance-based problem characterization and ranking combination methods
dc.typeArtigo
local.scopus.citations104
local.scopus.eid2-s2.0-84922691281
local.scopus.subjectAlgorithm selection
local.scopus.subjectClustering
local.scopus.subjectMeta-knowledge
local.scopus.subjectMeta-learning system
local.scopus.subjectProblem characterization
local.scopus.updated2024-05-01
local.scopus.urlhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=84922691281&origin=inward
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