How to validate my result of anomaly detection using k means clustering??
3 Ansichten (letzte 30 Tage)
Ältere Kommentare anzeigen
I had a synthetic data set with an artificially injected anomaly at some point. I wanted to detect that anomaly using unsupervised learning techniques. So, I ran some of the processing on the data and in the end finally used k means clustering to detect it. I used 3 clusters (coz that was the best solution possible) and finally got the locations of the added anomaly (using the distance metric from centroid) with some results for the other clusters as well.
Now how to critically evaluate the performance of my approach??
0 Kommentare
Akzeptierte Antwort
Image Analyst
am 18 Aug. 2017
How about just computing the percentage of time it correctly detected the anomaly?
If you wanted to go further you could very the distance from known centroids and compute the ROC curve.
Weitere Antworten (0)
Siehe auch
Kategorien
Mehr zu Statistics and Machine Learning Toolbox finden Sie in Help Center und File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!