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Ιδρυματικό Αποθετήριο [SANDBOX]
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/05583BB2-C7C4-4BD3-AC9F-F84509207436 | - |
| Αναγνωριστικό | https://doi.org/10.26233/heallink.tuc.68453 | - |
| Γλώσσα | en | - |
| Μέγεθος | 4.54 megabytes | en |
| Τίτλος | Performance of multivariate clustering methods in oil families' identification | en |
| Δημιουργός | Karavoulia Christina | en |
| Δημιουργός | Καραβουλια Χριστινα | el |
| Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Gaganis Vasileios | en |
| Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Γαγανης Βασιλειος | el |
| Συντελεστής [Επιβλέπων Καθηγητής] | Pasadakis Nikos | en |
| Συντελεστής [Επιβλέπων Καθηγητής] | Πασαδακης Νικος | el |
| Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Christopoulos Dionysios | en |
| Συντελεστής [Μέλος Εξεταστικής Επιτροπής] | Χριστοπουλος Διονυσιος | el |
| Εκδότης | Πολυτεχνείο Κρήτης | el |
| Εκδότης | Technical University of Crete | en |
| Ακαδημαϊκή Μονάδα | Technical University of Crete::School of Mineral Resources Engineering | en |
| Ακαδημαϊκή Μονάδα | Πολυτεχνείο Κρήτης::Σχολή Μηχανικών Ορυκτών Πόρων | el |
| Περίληψη | As science progresses, the need for analyzing multivariate data sets is growing by the minute. Multiple disciplines, either scientific or not, require the examination of large amounts of data, in a short period of time, in order to obtain useful information. During the recent few decades, multivariate statistical analysis methods have been developed, aiming to satisfy such purposes. This dissertation deals with the implementation of multivariate data analysis methods on a given data set, derived from oil family affiliations, which originate from Williston Basin of North America. In particular, Hierarchical Clustering, k-means and Principal Component analysis have been applied on four independent models, in an attempt to extract information regarding the oil-oil correlations among the samples under study. The models used on the exploration of the compositional information were the Saturated Fraction Compositional Model, the Saturated Fraction Ratios Model, the Gasoline Range Compositional Model and the Biomarkers Compositional Model. These standard statistical methods were found to be quite insufficient in classifying the sample set into distinct familial affiliations. For this reason, the need to examine the nature of the data set arose. Compositional data represent a category on their own as they are characterized by specific numerical properties which present significant consequences when being analyzed by standard multivariate techniques. The analysis of such type of data represents a whole new chapter in the world of statistics and the need for further examination on this matter is constantly growing. | en |
| Τύπος | Μεταπτυχιακή Διατριβή | el |
| Τύπος | Master Thesis | en |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
| Ημερομηνία | 2017-06-26 | - |
| Ημερομηνία Δημοσίευσης | 2017 | - |
| Θεματική Κατηγορία | Oil families' identification | en |
| Θεματική Κατηγορία | Multivariate clustering | en |
| Βιβλιογραφική Αναφορά | Christina Karavoulia, "Performance of multivariate clustering methods in oil families' identification", Master Thesis, School of Mineral Resources Engineering, Technical University of Crete, Chania, Greece, 2017 | en |
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