Usporedba metoda grupiranja primjenom programskog jezika Python

undergraduate thesis

• Title (english): Comparison of Clustering Methods Using Python Programming Language
• Author: Ian Golob
• Mentor: Goran Delač (mentor)
• Committee member: Goran Delač (predsjednik povjerenstva)
• Committee member: Marin Kačan (član povjerenstva)
• Committee member: Helena Čeović (član povjerenstva)
• Granter: University of Zagreb; Faculty of Electrical Engineering and Computing; Zagreb
• Defense date and country: 2023-07-05, Croatia
• Scientific / art field, discipline and subdiscipline: TECHNICAL SCIENCES; Computing

Abstract

U ovom radu opisana su i uspoređena četiri različita algoritma grupiranja podataka: K-sredina, Model Gaussove mješavine, BIRCH i Aglomerativno grupiranje. Podaci su prethodno obrađeni algoritmima pretprocesiranja: standardizacija, normalizacija i analiza glavnih komponenti (PCA). Za evaluaciju korištene su tri različite metrike: Randov indeks, Mjera međusobne informacije i V-Mjera. Algoritmi grupiranja su uspoređeni na temelju: evaluacijskih metrika, vremena izvođenja, utjecaja pretprocesiranja te utjecaja šuma na rezultate.

Abstract (english)

In this paper, four different data clustering algorithms were described and compared: K-means, Gaussian Mixture Model, BIRCH and Agglomerative Clustering. The data was preprocessed using four different methods: standardization, normalization and Principal Component Analysis (PCA). Three different metrics were used for evaluation: Rand Index, Mutual Information Score and V-Measure. The clustering algorithms were compared based on evaluation metrics, execution time, the impact of preprocessing and the impact of noise on the results.

• Keywords: nenadzirano strojno učenje; analiza podataka; Python; scikit-learn; mjere evaluacije grupiranja; algoritmi grupiranja; pretprocesiranje podataka
• Keywords (english): unsupervised machine learning; data analysis; Python; scikit-learn; clustering evaluation metrics; clustering algorithms; data preprocessing
• Language: croatian
• URN:NBN: urn:nbn:hr:168:132059
• Study programme: Title: Computing; Study programme type: university; Study level: undergraduate; Academic / professional title: sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva (sveučilišni/a prvostupnik/ prvostupnica (baccalaureus/baccalaurea) inženjer/inženjerka računarstva)
• Type of resource: Text
• File origin: Born digital
• Access conditions: Closed access
• Created on: 2023-10-11 08:54:44