Usporedba postupaka dubinske analize primjenjenih nad biološkim podacima

Domazet Lošo, Mirjana

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scientific master's theses

Usporedba postupaka dubinske analize primjenjenih nad biološkim podacima

Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing, 2006. urn:nbn:hr:168:969779

Domazet Lošo, Mirjana

University of Zagreb
Faculty of Electrical Engineering and Computing
Department of Applied Computing

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APA 6th Edition

Domazet Lošo, M. (2006). Usporedba postupaka dubinske analize primjenjenih nad biološkim podacima (Scientific master's theses). Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing. Retrieved from https://urn.nsk.hr/urn:nbn:hr:168:969779

MLA 8th Edition

Domazet Lošo, Mirjana. "Usporedba postupaka dubinske analize primjenjenih nad biološkim podacima." Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, 2006. https://urn.nsk.hr/urn:nbn:hr:168:969779

Chicago 17th Edition

Harvard

Domazet Lošo, M. (2006). 'Usporedba postupaka dubinske analize primjenjenih nad biološkim podacima', Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, accessed 17 March 2025, https://urn.nsk.hr/urn:nbn:hr:168:969779

Vancouver

Domazet Lošo M. Usporedba postupaka dubinske analize primjenjenih nad biološkim podacima [Scientific master's theses]. Zagreb: University of Zagreb, Faculty of Electrical Engineering and Computing; 2006 [cited 2025 March 17] Available at: https://urn.nsk.hr/urn:nbn:hr:168:969779

IEEE

M. Domazet Lošo, "Usporedba postupaka dubinske analize primjenjenih nad biološkim podacima", Scientific master's theses, University of Zagreb, Faculty of Electrical Engineering and Computing, Zagreb, 2006. Available at: https://urn.nsk.hr/urn:nbn:hr:168:969779

Cite this item: https://urn.nsk.hr/urn:nbn:hr:168:969779

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Metadata

Title	Usporedba postupaka dubinske analize primjenjenih nad biološkim podacima
Title (english)	Comparison of Data Mining Techniques Applied on Biological Data
Author	Mirjana Domazet Lošo
Mentor	Mirta Baranović (mentor)
Committee member	Damir Kalpić (predsjednik povjerenstva)
Committee member	Mirta Baranović (član povjerenstva)
Committee member	Mladen Varga (član povjerenstva)
Granter	University of Zagreb Faculty of Electrical Engineering and Computing (Department of Applied Computing) Zagreb
Defense date and country	2006-06-13, Croatia
Scientific / art field, discipline and subdiscipline	TECHNICAL SCIENCES Computing
Universal decimal classification (UDC)	004 - Computer science and technology. Computing. Data processing
Abstract	Biološki procesi opisani su mnoštvom podataka prikupljenih promatranjima i eksperimentima. Kako bi se iz složene strukture bioloških podataka izlučile nove informacije koriste se metode dubinske analize podataka. S obzirom na složenost strukture podataka, prije same dubinske analize podatke je potrebno adekvatno pripremiti. Priprema podataka uključuje pročišćavanje podataka, integraciju i transformaciju podataka. U ovom je radu poseban naglasak stavljen na transformaciji podataka, posebno na metodama smanjivanja dimenzijske složenosti podataka. Podaci korišteni u praktičnom dijelu rada odnose se na gene za koje je potrebno predvidjeti utječu li na promatrani biološki sustav ili ne. S obzirom na problematiku, proučeni su postupci dubinske analize podataka s naglaskom na metode razvrstavanja (klasifikacije) podataka. Uspoređeni su odabrani algoritmi razvrstavanja, te je objašnjeno što je utjecalo na bolje, odnosno lošije rezultate odabranih algoritama. Uočeno je da najbolje rezultate daju algoritam VFI i algoritam 1-razred SVM.
Abstract (english)	Biological processes are described using huge amount of data collected through the observation and experiments. Data mining is used for interesting knowledge extraction. Due to data complexity, data mining process should succeed the process of cleaning data, integration of data from different sources and transformation of data. Considering the problems involved with the data set used in this work, the emphasis is set on the transformation of data, especially on the feature selection. Considering the given data set, the goal of the work is to learn a model that can predict whether the particular gene influences the specified biological system. The overview of data mining techniques is given with the special attention to classification algorithms, because the model for the given data set is build using classification algorithms. The comparison of classification algorithms showed that the best results are provided using VFI (Voting Feature Interval) algorithm and one-class SVM (Support Vector Machines) algorithm.
Keywords
Keywords (english)
Language	croatian
URN:NBN	urn:nbn:hr:168:969779
Study programme	Title: Postgraduate master programe in computing Study programme type: university Study level: postgraduate Academic / professional title: magistar inženjer računarstva (magistar inženjer računarstva)
Type of resource	Text
File origin	Born digital
Access conditions	Closed access
Terms of use
Repository	FER Repository
Created on	2019-07-17 13:44:35