Actionable Knowledge Discovery (AKD) is a crucial aspect of data mining that
is gaining popularity and being applied in a wide range of domains. This is
because AKD can extract valuable insights and information, also known as
knowledge, from large datasets. The goal of this paper is to examine different
research studies that focus on various domains and have different objectives.
The paper will review and discuss the methods used in these studies in detail.
AKD is a process of identifying and extracting actionable insights from data,
which can be used to make informed decisions and improve business outcomes. It
is a powerful tool for uncovering patterns and trends in data that can be used
for various applications such as customer relationship management, marketing,
and fraud detection. The research studies reviewed in this paper will explore
different techniques and approaches for AKD in different domains, such as
healthcare, finance, and telecommunications. The paper will provide a thorough
analysis of the current state of AKD in the field and will review the main
methods used by various research studies. Additionally, the paper will evaluate
the advantages and disadvantages of each method and will discuss any novel or
new solutions presented in the field. Overall, this paper aims to provide a
comprehensive overview of the methods and techniques used in AKD and the impact
they have on different domains