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dc.rights.licenseAll rights reserveden_US
dc.contributor.advisorVicente, Luis M.
dc.contributor.authorCruz Rodríguez, David
dc.date.accessioned2020-07-20T19:04:29Z
dc.date.available2020-07-20T19:04:29Z
dc.date.issued2012
dc.identifier.citationCruz Rodríguez, D. (2012). Parallel evaluation of large scale hierarchical clustering results [Unpublished manuscript]. Graduate School, Polytechnic University of Puerto Rico.en_US
dc.identifier.urihttp://hdl.handle.net/20.500.12475/426
dc.descriptionDesign Project Article for the Graduate Programs at Polytechnic University of Puerto Ricoen_US
dc.description.abstractAbstract ⎯ Data clustering refers to the automatic grouping of object based on their similarity, i.e., similar objects should be in the same group and dissimilar objects should be in different groups. In particular, for hierarchical clustering algorithms there is also the notion of a hierarchy in which the objects and the cluster fit. Clustering is a fundamental task in data mining, machine learning, information retrieval, bioinformatics, and image analysis, among others. It is important to evaluate the result of clustering algorithms. However most evaluations approaches are geared towards nonhierarchical clustering approaches; this research explores how to use traditional validity measures to evaluate and assess hierarchical clustering results. Key Terms ⎯ Clustering, Data Clustering, Hierarchical Clustering, and Validity Measures.en_US
dc.language.isoenen_US
dc.publisherPolytechnic University of Puerto Ricoen_US
dc.relation.ispartofComputer Engineering;
dc.relation.ispartofseriesSpring-2012;
dc.relation.haspartSan Juanen_US
dc.subject.lcshCluster analysisen_US
dc.subject.lcshData miningen_US
dc.subject.lcshPolytechnic University of Puerto Rico--Graduate students--Research
dc.titleParallel Evaluation of Large Scale Hierarchical Clustering Resultsen_US
dc.typeArticleen_US
dc.rights.holderPolytechnic University of Puerto Rico, Graduate Schoolen_US


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