EXPLORING CLASSIFICATION ON AUTONOMOUSLY GENERATED DATASET
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Date
2023
Authors
Bauyrzhanuly, Sultan
Journal Title
Journal ISSN
Volume Title
Publisher
School of Engineering and Digital Sciences
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.
Description
Keywords
Type of access: Open Access, data, Dataset
Citation
Bauyrzhanuly, S. (2023). Exploring Classification on Autonomously Generated Dataset. School of Engineering and Digital Sciences