A Literature Review of Data Mining Techniques and Applications for CA1

This assignment is a literature review focusing on data mining techniques and applications. It explores various aspects of data mining, including models, frameworks, and applications in different industries such as social media, finance, retail, and telecommunications. The review covers key themes, providing an overview, critical evaluation, and conclusions based on research. The assignment also touches upon ethical considerations, machine learning algorithms for big data, and methods for specific domains. The paper adheres to Harvard style referencing, a 12pt font, and a word count of approximately 3000 words (excluding the cover page, table of contents, and bibliography), and includes a marking scheme that evaluates the relevance of the chosen topic, the quality of selected material, critical evaluation, and paper development. The review aims to provide a comprehensive understanding of the current state of data mining and its future directions.

Data & Web Mining CA1
INDIVIDUAL ASSIGNMENT: Literature Review
For the purpose of this report, you are expected to carry out research to produce a Literature Review
somehow related to one of the following topics:
 Data mining and Knowledge Discovery
 Mining Data Techniques and Applications
This assignment should focus on particular aspects of your chosen topic and provide an in-depth review
of these particular aspects. Some example topics of interest include, but are not limited to:
 Models and characteristics of state of the art Data Mining applications – are there certain
frameworks, toolkits, languages etc. that result in more successful Data Mining results?
 Data Mining activities in social media.
 Text & Web mining
 Data Mining in Financial Analysis.
 Data Mining in Retail Industry.
 Data Mining in Telecommunication Industry.
 Data Mining in Biological Analysis.
 Other Scientific Applications.
 Intrusion Detection.
 Development of Machine Learning algorithms to handle Big Data
 Ethical considerations regarding Data Mining vis-a-vis data protection and data privacy
 Methods of data mining for specific domains / problem areas / use cases
 Any data mining method not covered in the course: e.g. neural networks, random forests,
clustering methods other than k-means, etc.
Your paper should include:
 An overview of the topic you have chosen
 Individual sections for each of the key themes
 Conclusions you have drawn based on your research
 The paper should be of 3000 words - approx 6 PAGES (not including Cover Page, Table of
Content & Bibliography) in length and should have the following characteristics:
− 12pt font / Arial / Normal Line Spacing
− Harvard Style Referencing
− Images, charts, quotations, etc. should make up NO MORE than 25% of the contents
You should draw on appropriate academic literature for your review. Tools like Google Scholar or NCI
library will be of key value to achieve this.

MARKING SCHEME:
Paper Topic: 10%
 Relevance of chosen topic
 Elicitation of appropriately formed review question
Quality of selected material: 35%
 Competency and consideration into choice of sources
 Selection of appropriate sources
 Quality of information dissemination
Critical Evaluation: 40%
 Approach towards addressing the topic
 Evidence of critical evaluation of material
 Quality of argument, discussion, interpretation
Paper Development: 15%
 Quality in method, organisation, interpretation and resolution of paper
 Adequate and consistent referencing and citation