Publication:
Document classification based on kNN algorithm by term vector space reduction

dc.citedby8
dc.contributor.authorMoldagulova A.en_US
dc.contributor.authorSulaiman R.B.en_US
dc.contributor.authorid57160071400en_US
dc.contributor.authorid25825633600en_US
dc.date.accessioned2023-05-29T06:49:56Z
dc.date.available2023-05-29T06:49:56Z
dc.date.issued2018
dc.descriptionClassification (of information); Data handling; Data mining; Information retrieval systems; Learning algorithms; Text processing; Vectors; Document Classification; Space reductions; Text classifiers; Text mining; Textual data; Unstructured data; Vector spacesen_US
dc.description.abstractNowadays there is an increasing interest in the area of unstructured data analysis. The vast majority of unstructured data belongs to unstructured text data. Retrieving useful information from huge volume of unstructured text data is very challenging task. Text mining is a thought-provoking research area as it tries to discover knowledge from unstructured text. This paper deals with methods used for handling unstructured text data in particular document classification problems. Most document classification methods based on term vector space model of representation of unstructured textual data. The term vector space model is easy to implement, provides uniform representation for documents. However feature space for a large collection of documents can reach millions and be sparse. One of the issues is to reduce the dimension of the term-document matrix. In this research we proposed an approach for reduction of term vector space in KNN algorithm. � ICROS.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo8571540
dc.identifier.epage391
dc.identifier.scopus2-s2.0-85060480043
dc.identifier.spage387
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85060480043&partnerID=40&md5=d52c11efb08aee7a1a10a37b9778cd46
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/23499
dc.identifier.volume2018-October
dc.publisherIEEE Computer Societyen_US
dc.sourceScopus
dc.sourcetitleInternational Conference on Control, Automation and Systems
dc.titleDocument classification based on kNN algorithm by term vector space reductionen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
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