  In recent years, researchers have become increasingly interested in outlying
aspect mining. Outlying aspect mining is the task of finding a set of
feature(s), where a given data object is different from the rest of the data
objects. Remarkably few studies have been designed to address the problem of
outlying aspect mining; therefore, little is known about outlying aspect mining
approaches and their strengths and weaknesses among researchers. In this work,
we have grouped existing outlying aspect mining approaches in three different
categories. For each category, we have provided existing work that falls in
that category and then provided their strengths and weaknesses in those
categories. We also offer time complexity comparison of the current techniques
since it is a crucial issue in the real-world scenario. The motive behind this
paper is to give a better understanding of the existing outlying aspect mining
techniques and how these techniques have been developed.
