EXPLORING CLASSIFICATION ON AUTONOMOUSLY GENERATED DATASET

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.