  Outlier is the term that indicates in statistics an anomalous observation,
aberrant, clearly distant from others collected observations. The outliers are
the subject to animated discussions in various contexts with regard to be or
not to be considered in the average evaluations. Outliers can become a precious
source of information, on condition that be able to accurately identify the
presence in the reference datasets. The need to identify the presence of
clustered outliers in a dataset not previously treated could argue for a fuzzy
clustering, emphasized by using the quadratic Euclidean distance as similarity
measure. For interesting and useful results, it should be inclined a
possibilistic clustering approach, where the term 'possibilistic' means, always
in mathematical rigor, a component of interpretation of values that point out
anomalous cases. The crisp method does not allow it, the fuzzy method introduce
it, the possibilistic one use it. This is a very simple paper with divulgative
purposes, addressed especially to students, but not only.
