RES709 UOPX A Literature Review Comparing the Four Scholarly Research

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  1. The literature review serves as an important purpose in any dissertation, research article, report or proposal. It supports the need for the study and convinces readers that the content area has been reviewed in depth, and there is clearly a research problem to be addressed and a gap in knowledge needing to be filled. A literature review should contain current and classic references, cite experts in field and synthesize the relevant literature while identifying the strengths and weaknesses of previous studies.
    Consider a research interest within your degree program.
    Select four recently published research articles related to your research interest; two quantitative and two qualitative research studies. You may substitute a mixed methods study for one of the qualitative or quantitative studies. Ensure that each of the studies you selected was published in a peer reviewed journal.
    Review the Literature Review Presentation to understand synthesis of literature.
    Write a synthesized 1,400-to 1,750-literature review in which you compare and contrast the four scholarly research articles.
    Consider the following while constructing a logical and critical literature review:
    • What is already known about the research topic you are interested in?
    • What is not known about your topic; where is there a gap in the literature?
    • What further research might be worth pursuing in this area?

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ProfDwayne01
School: Purdue University

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A literature Review Comparing the Four Scholarly Research Articles.
Student’s Name
Course Title
October 14, 2019

Bibliography
This literature review provides a summary of insights from articles that relate to the
selected course topic on Data Mining and Knowledge Discovery in Databases. The literature
sources seek to provide insight on the knowledge presented in these articles paving way for further
research in the field of data mining and KDD.
A recent article by Azevedo, 2019, provides a clear definition of the relationship between
data mining and knowledge discovery in databases. The article explores KDD and provides
information on how this broad process of knowledge exploration in data came about. It also sheds
light on application of data mining methods. Data mining is an analytical process that allows the
most autonomous and efficient identification and description of interesting data patterns from large
databases. In this paper, KDD is presented as an interdisciplinary approach that uses methods
from computer science and statistics. The paper casts light on the need to differentiate the so-called
knowledge discovery in databases - Knowledge Discovery in Data Bases (KDD). Many works
attribute exactly the same meaning to these terms. That is not quite true. Knowledge discovery in
databases is a more comprehensive process involving data mining methods. It also includes tasks
such as preparing the data, checking for interference, or visualizing the results. According to the
article, the goal of data mining is usually the generation of hypotheses from a database, a so-called
hypothesis-free bottom-up approach (Azevedo, 2019).
The article sheds light on how fundamental interdisciplinary study in machine learning has
been done since the 1990s. The authors look at the widespread exploitation of KDD discovery in
assembling related knowledge. Knowledge discovery in databases is aimed at closing the gap
between the often extensive data assets in companies, for example in data warehouses, and the
increasing need for action-relevant knowledge. For this purpose, various data mining methods are

used, including the adaptive control systems, in the narrower neuro-fuzzy s...

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