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Document clustering using swarm intelligence

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bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorAli, Abu Mohammad Hammad
dc.contributor.authorRizwan, Md. Adeeb
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2013-04-30T18:14:23Z
dc.date.available2013-04-30T18:14:23Z
dc.date.copyright2012
dc.date.issued2012-12
dc.descriptionCataloged from PDF version of thesis report.
dc.descriptionIncludes bibliographical references (page 27).
dc.descriptionThis thesis report is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2012.en_US
dc.description.abstractWith the recent rise in electronic data, and the pressing time needs of daily life, there is scope for a good document clustering system that can divide a set of documents into similar topic clusters. A lot of different algorithms have been attempted towards this end, from statistical learning methods to neural networks. In more recent years, there has been a growing interest in collective intelligence as often displayed in nature by ants and birds. We would like to do a survey of this last field, and look for possible applications of algorithms in this area for the document clustering problem.en_US
dc.description.degreeBachelor of Science in Computer Science and Engineering
dc.description.statementofresponsibilityMd. Adeeb Rizwan
dc.format.extent27 pages
dc.identifier.otherID 09201029
dc.identifier.urihttp://hdl.handle.net/10361/2381
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBRAC University thesis are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectComputer science and engineering
dc.titleDocument clustering using swarm intelligenceen_US
dc.typeThesisen_US

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