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An attempt at the network anomaly detection task using manually implemented k-means, spectral clustering and DBSCAN algorithms, with manually implemented evaluation metrics (precision, recall, f1-score and conditional entropy) used to evaluate these algorithms.
This project compares between different clustering algorithms: K-Means, Normalized Cut and DBSCAN algorithms for network anomaly detection on the KDD Cup 1999 dataset
Segmentation based on similarity measure including Intensity difference and Distance of pixels. Also effect of rotation and addition of gaussian noise on segmentation is visualized using Matplotlib.