Text Clustering Using LucidWorks and Apache Mahout

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dc.contributorVirginia Tech. Digital Library Research Laboratoryen
dc.contributorVirginia Tech. Department of Computer Scienceen
dc.contributor.authorChen, Liangzheen
dc.contributor.authorLin, Xiaoen
dc.contributor.authorWood, Andrewen
dc.contributor.departmentDigital Library Research Laboratoryen
dc.contributor.departmentComputer Scienceen
dc.contributor.editorFox, Edward A.en
dc.contributor.editorChitturi, Kiranen
dc.contributor.editorKanan, Tareken
dc.date.accessioned2015-05-22T14:18:55Zen
dc.date.available2015-05-22T14:18:55Zen
dc.date.issued2012-11-17en
dc.description.abstractThis module introduces algorithms and evaluation metrics for flat clustering. We focus on the usage of LucidWorks big data analysis software and Apache Mahout, an open source machine learning library in clustering of document collections with the k-means algorithm.en
dc.description.notesCS 5604: Information Storage and Retrievalen
dc.format.extent12 pagesen
dc.format.mimetypeapplication/pdfen
dc.identifier.urihttp://hdl.handle.net/10919/52539en
dc.identifier.urlhttp://curric.dlib.vt.edu/modDev/lucidworks_modules/CS5604F2012Module-LucidWorks-Clustering.pdfen
dc.language.isoen_USen
dc.relation.ispartofseriesDigital Library Curriculum Projecten
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectComputer scienceen
dc.subjectDigital librariesen
dc.subjectText clusteringen
dc.subjectLucidworksen
dc.subjectApache mahouten
dc.titleText Clustering Using LucidWorks and Apache Mahouten
dc.typeLearning objecten
dc.type.dcmitypeTexten

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