samahussien7 / handwritten-digit-classification-using-k-means-clustering

Using K-Means clustering, with feature extraction comparison between centroid and chaincode methods.. The script implements K-Means clustering from scratch, performs feature extraction using both centroid and chaincode techniques, evaluates classification accuracy, and compares the effectiveness of the two feature extraction methods.

Handwritten-Digit-Classification-Using-K-Means-Clustering

Using K-Means clustering, with feature extraction comparison between centroid and chaincode methods.. The script implements K-Means clustering from scratch, performs feature extraction using both centroid and chaincode techniques, evaluates classification accuracy, and compares the effectiveness of the two feature extraction methods.

Steps:

Load MNIST Dataset:

Retrieves the MNIST dataset containing images of handwritten digits along with their corresponding labels.

Feature Extraction with Centroid Method:

Computes centroids for each image. Uses centroid coordinates as features for clustering.

K-Means Clustering:

Implements K-Means clustering algorithm without relying on built-in functions. Clusters the data based on the extracted centroid features.

Classification and Accuracy Evaluation (Centroid Method):

Assigns cluster labels to each image and evaluates classification accuracy. Computes accuracy metric to assess the performance of K-Means clustering with centroid feature extraction.

Feature Extraction with Chaincode Method:

Computes chaincode for each image block. Utilizes chaincode features for clustering.

K-Means Clustering (Chaincode Method):

Applies K-Means clustering using chaincode features.

Classification and Accuracy Evaluation (Chaincode Method):

Assigns cluster labels to images based on chaincode features and evaluates accuracy. Computes accuracy metric to compare with the centroid method.

Comparison of Feature Extraction Methods:

Analyzes and compares the accuracy achieved with centroid and chaincode feature extraction. Determines which feature extraction method yields better performance with K-Means clustering.