Unsupervised Learning upon a column of dataset (graph shown)
A few observations/questions/considerations:


Wikipedia: Unsupervised learning is a type of self-organized Hebbian learning that helps find previously unknown patterns in data set without pre-existing labels.
Being an anomaly is a label.
Why not just Z-score it? https://en.wikipedia.org/wiki/Standard_score#Z-test . Calculate distribution: $\mu$ and $\sigma$, and check if value is
within $\mu$ +/- 1.96 $\sigma$, $95\%$ chance it is from the normal distribution of the numbers in the column.
Can the Value column be something else than numbers? (E.g. color names)
Or could it be a time series with drift?




Edit:

Under the hood, Machine Learning is basically a set of smart statistics and decisions on subsets of columns and rows. But with only one column and no time series, there is nothing smart to subset or decide. Then essentially, it is an exceptional Z-score for the elements. You have to decide on how many $\sigma$ you want.