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Automatic document clustering and indexing of multiple documents using KNMF for feature extraction through Hadoop and lucene on big data

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dc.citedby3
dc.contributor.authorLaxmi Lydia E.en_US
dc.contributor.authorSharmili N.en_US
dc.contributor.authorNguyen P.T.en_US
dc.contributor.authorHashim W.en_US
dc.contributor.authorMaseleno A.en_US
dc.contributor.authorid57196059278en_US
dc.contributor.authorid57191575400en_US
dc.contributor.authorid57216386109en_US
dc.contributor.authorid11440260100en_US
dc.contributor.authorid55354910900en_US
dc.date.accessioned2023-05-29T07:27:55Z
dc.date.available2023-05-29T07:27:55Z
dc.date.issued2019
dc.descriptionAutomatic indexing; Big data; Cluster analysis; Extraction; Factorization; Indexing (of information); Information retrieval; K-means clustering; Natural language processing systems; Open source software; Open systems; Pattern matching; Software quality; Software testing; Text mining; Hadoop; Key phrase extractions; Map-reduce; Pattern-matching technique; Porters; Pre-processing algorithms; Software environments; Unlabeled; Matrix algebraen_US
dc.description.abstractThe existence of unlabeledtext data in documents has become larger and excavating such datasets is a provocative task. The objective of Big Data is to store, retrieve and analyse multipletext documents. Problem Statement:The retrieval of the identical data over large databases is of major concern. Existing Solution:Existing problem is solved by Full-Text Search (FTS) which means pattern matching technique that allows searching of multiple keywords at specific time.Proposed Solution: In this paper, we consider multiple text documents as input and processed using text mining pre-processing algorithms like Key Phrase extraction, Porters stemming for tokenizing and TF_IDF toobtain all non-negative values. These values further processed to get matrix data throughNonnegative matrix factorization (NMF). On performing NMF, K-means algorithmis upgraded with NMF to obtain quality clusters of data sets.Performances of the algorithms are tested using Newsgroup20 data in Open Source Hadoop software environment which also analyses the performance of the MapReduce framework. The final outcome is to generate clusters and index them for the Newsgroup20dataset. Later on, Apache Lucene is presented for automatic document clustering with aGUI interface developed for indexing. Thus, this proposed algorithm resultsby improving the performance of document clustering through Map Reduce framework in Hadoop. � 2019 Mattingley Publishing. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.epage1130
dc.identifier.issue11-Dec
dc.identifier.scopus2-s2.0-85079574447
dc.identifier.spage1107
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85079574447&partnerID=40&md5=1ed7ff4baa70eeccef9e5755fa21fcec
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/24853
dc.identifier.volume81
dc.publisherMattingley Publishingen_US
dc.sourceScopus
dc.sourcetitleTest Engineering and Management
dc.titleAutomatic document clustering and indexing of multiple documents using KNMF for feature extraction through Hadoop and lucene on big dataen_US
dc.typeArticleen_US
dspace.entity.typePublication
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