diplomska naloga
Blaž Novak (Author), Ivan Bratko (Mentor), Dunja Mladenić (Co-mentor)

Abstract

Odkrivanje tematik v zaporedju besedil in sledenje njihovim spremembam

Keywords

razvrščanje;rudarjenje v podatkovnih tokovih;računalništvo;univerzitetni študij;diplomske naloge;

Data

Language: Slovenian
Year of publishing:
Typology: 2.11 - Undergraduate Thesis
Organization: UL FRI - Faculty of Computer and Information Science
Publisher: [B. Novak]
UDC: 004(043.2)
COBISS: 6723668 Link will open in a new window
Views: 795
Downloads: 163
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Metadata: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Other data

Secondary language: English
Secondary title: [Topic detection and tracking in a stream of documents]
Secondary abstract: A challenge created by the recent development in information technology is that people are often faced with an overwhelming amount of information available to them, with blogs presenting the latest and most abundant source of such information. In this thesis, I approach the problem from a standpoint of organizing the newly created information into sensible groups. The first part of the thesis is an overview of the state of the art in the areas relevant to the problem and an analysis of shortcomings of different methods. The main contribution is the development of a new algorithm that pieces together various ideas presented in the first part. It is an online hierarchical clustering algorithm that is capable of incremental model updates that support the addition and also the removal of documents. The structure of the model is adapted after each step to better reflect the structure of the currently observed world. The model can also be optimized while waiting for new events. Some experiments to test the properties of the new algorithm were performed using simulated data streams created from the Reuters Corpus Volume 1 dataset. I have found that the basic assumptions about time complexity and the ability to adapt the model are correct and that the algorithm performs surprisingly well for a range of different inputs.
Secondary keywords: clustering;stream mining;computer science;diploma;
File type: application/pdf
Type (COBISS): Undergraduate thesis
Thesis comment: Univerza v Ljubljani, Fakulteta za računalništvo in informatiko
Pages: 63 str.
ID: 23829061