**box plot**where outliers will be visible in dots or points and majority of the data will be inside the box. Multivariate outliers can be found out by looking at n-dimensional feature set which is difficult for humans. Though bivariate outliers can be detected using scatter plots. Automated methods to detect outliers include Z-score, Probabilistic Modeling, Clustering, Linear Regression models etc.

**Z-score**which indicates how many standard deviations far is the data point from the mean assuming gaussian distribution. Z-score is useful for parametric distributions in low dimensional space.

**DBSCAN**is a density based clustering method useful for outlier detection. Points which do not get assigned to any cluster or form their own clusters are labelled outliers.

**Isolation forest**is designed for outlier detection which is based on decision tree and more precisely random forests. This follows the mechanism of decision tree by splitting the dataset on random feature at first. At every split, this split is repeated with other random features. Number of splittings made by the algorithm is the path length for a fixed data point. Outliers are expected to have shorter path lengths and stay closer to the root.

**OneClass SVM**, variant of SVM, is an outlier detection method. SVM is sensitive to outliers which is used to its advantage by OneClass SVM.

Please visit this page for more explanation on DBSCAN and Isolation forest.