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Title:KLASIFIKACIJA SPLETNIH NOVIC S POMOČJO METOD PODATKOVNEGA RUDARJENJA V BESEDILU
Authors:Sandić, Mitja (Author)
Podgorelec, Vili (Mentor) More about this mentor... New window
Files:.pdf UNI_Sandic_Mitja_2012.pdf (2,81 MB)
 
Language:Slovenian
Work type:Undergraduate thesis (m5)
Typology:2.11 - Undergraduate Thesis
Organization:FERI - Faculty of Electrical Engineering and Computer Science
Abstract:Pogosto uporabniki interneta dobijo rezultat iskanja, ki vsebuje širok spekter dokumentov, a le nekaj je teh, ki so pomembni za njihove potrebe po informacijah. Za reševanje tega problema vedno več sistemov iskano informacijo organizira po ključu, ki je vsebinsko povezan s podatki. Poglejmo dva glavna avtomatska pristopa k organizaciji informacij: interaktivno povezovanje rezultatov iskanja (angl. clustering) in predkategoriziranje dokumentov, kjer zagotovimo hierarhično strukturo iskanja. Oba pristopa zahtevata v realnem svetu aplikacij učinkovite algoritme, ki se ubadajo s problematiko predstavitev dokumentov s sto ali več tisoč besedami. Problem se pojavi v tem, kako analizirati in obdelati takšno količino podatkov. V diplomskem delu bomo predstavili, kako dokumente pravilno pripraviti (zmanjšati vsebino dokumentov), ne da bi pri tem negativno vplivali na samo klasifikacijo. Poleg tega pa, kako z dodatnimi parametri zagotoviti še boljšo klasifikacijo.
Keywords:podatkovno rudarjenje v besedilih, klasifikacija, iskanje informacij.
Year of publishing:2012
Publisher:[M. Sandić]
Source:Maribor
UDC:004.6:004.8(043.2)
COBISS_ID:16177686 Link is opened in a new window
NUK URN:URN:SI:UM:DK:SVQQCGKG
Views:1077
Downloads:168
Metadata:XML RDF-CHPDL DC-XML DC-RDF
Categories:KTFMB - FERI
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Secondary language

Language:English
Title:WEB NEWS CLASSIFICATION USING TEXT MINING TECHNIQUES
Abstract:Often users receive search results, which contain a wide range of documents, only some of which are relevant to their information needs. To address this problem, ever more systems not only locate information for users, but also organize that information on their behalf. We look at two main automatic approaches to information organization: interactive clustering of search results and pre-categorizing documents to provide hierarchical browsing structures. To be feasible in real world applications, both of these approaches require accurate yet efficient algorithms. Yet, both suffer from the curse of dimensionality -documents are typically represented by hundreds or thousands of words (features) which must be analyzed and processed during clustering or classification. In this paper, we discuss how to prepare (feature reduction techniques) documents and not to affect a classification. Also how with addition parameters improve efficiency as well as accuracy.
Keywords:text mining, classification, information retrieval.


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