# Can I use anomaly detection models as outliers and novelty detection?

Several books that I have read do not distinguish the several models that exist for anomaly and outlier detection.

After I read about these models, I have chosen to detect anomalous events on unsupervised and supervised data the following algorithms:

Unsupervised data:

1. K-means
2. DBSCAN
3. One class support vector machine

Supervised data:

1. Support vector machine

Let's imagine that these models were really good at detecting anomalous points. Now I want to detect outliers and novel events. Can I use the same algorithms? If so, in what sense it differs using anomaly, outlier, and novelty detection models?

In outlier and novelty detection I need to remove anomalous points before applying a model?