Pruning: Outlier Candidate Selection I am using method 3.3 described in Outlier Detection using Isolation Forest and Local Outlier
Factor .
It states:
Specify a dataset: $={_1, _2, ..., _}$.
Here,  is the sample number of . $_$ is an attribute in $$, and $_={_1,
_2, ..., _}.$ $_$
is a certain data value of the attribute $_$
.
The outlier coefficient of the attribute is defined as:

Here, $\bar{}$ is the mean of the attribute $_$ and $_$
is used to
measure the degree of dispersion of the attribute $_$
. Calculate
the outlier coefficient of each attribute in the dataset, and
get the outlier coefficient vector $_$ of the dataset, which is
recorded as:
$_ = _1, _2, ..., _n$
However, I do not understand what value to select for $x_j$.  What does a 'certain data value' mean in this case?
I suspect this paper has typos (see e.g., line 2 of Algorithm 1).  If so, you're going to have to guess from context what they might have meant.
It's possible that they might have meant to write
$$fd_i = \frac{\sqrt{\frac{\sum_j (x_j - \bar{x})^2}{n}}}{\bar{x}}$$
In other words: the ratio between the standard deviation and the mean of that attribute.
Who knows; this is just a guess.  You might try contacting the authors.