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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

Institutional repository: FER Repository

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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

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

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

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 03 May 2024, https://urn.nsk.hr/urn:nbn:hr:168:969779

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 2024 May 03] Available at: https://urn.nsk.hr/urn:nbn:hr:168:969779

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

Metadata
TitleUsporedba postupaka dubinske analize primjenjenih nad biološkim podacima
Title (english)Comparison of Data Mining Techniques Applied on Biological Data
AuthorMirjana Domazet Lošo
MentorMirta Baranović (mentor)
Committee memberDamir Kalpić (predsjednik povjerenstva)
Committee memberMirta Baranović (član povjerenstva)
Committee memberMladen Varga (član povjerenstva)
GranterUniversity of Zagreb
Faculty of Electrical Engineering and Computing
(Department of Applied Computing)
Zagreb
Defense date and country2006-06-13, 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)
Languagecroatian
URN:NBNurn:nbn:hr:168:969779
Study programmeTitle: Postgraduate master programe in computing
Study programme type: university
Study level: postgraduate
Academic / professional title: magistar inženjer računarstva (magistar inženjer računarstva)
Type of resourceText
File originBorn digital
Access conditionsClosed access
Terms of useLicence In copyright icon
Created on2019-07-17 13:44:35