EXPLORING CLASSIFICATION ON AUTONOMOUSLY GENERATED DATASET

Bauyrzhanuly, Sultan

EXPLORING CLASSIFICATION ON AUTONOMOUSLY GENERATED DATASET

dc.contributor.author	Bauyrzhanuly, Sultan
dc.date.accessioned	2023-05-24T08:15:50Z
dc.date.available	2023-05-24T08:15:50Z
dc.date.issued	2023
dc.description.abstract	In the era of exponential data growth, the organization and labeling of data play crucial roles. Unsupervised cluster analysis can be utilized to initially group the raw, unlabeled data obtained from a large dataset. This thesis explores the impact of various clustering algorithms, K-Means, DBSCAN, and Gaussian Mixture Models, on the performance of a supervised classification model, specifically AlexNet. The primary objective of the study is to evaluate the classification results on a subset of Places365 dataset after applying different clustering algorithms during the preprocessing phase. Through a series of experiments, we demonstrate that the choice of clustering algorithm significantly influences the performance of the classification model.	en_US
dc.identifier.citation	Bauyrzhanuly, S. (2023). Exploring Classification on Autonomously Generated Dataset. School of Engineering and Digital Sciences	en_US
dc.identifier.uri	http://nur.nu.edu.kz/handle/123456789/7066
dc.language.iso	en	en_US
dc.publisher	School of Engineering and Digital Sciences	en_US
dc.rights	Attribution-NonCommercial-ShareAlike 3.0 United States	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/3.0/us/	*
dc.subject	Type of access: Open Access	en_US
dc.subject	data	en_US
dc.subject	Dataset	en_US
dc.title	EXPLORING CLASSIFICATION ON AUTONOMOUSLY GENERATED DATASET	en_US
dc.type	Master's thesis	en_US
workflow.import.source	science