COMP40370 Data Mining

Academic Year 2021/2022

The course is structured in such a way as to present important concepts of data mining and how these concepts are implemented and used in real-world applications. The key idea behind this course is to integrate the theory and practice of data mining with many references to real-world problems and cases to illustrate the concepts and the implementation issues as we go through the lectures. The first chapter is devoted to a brief introduction to some background information needed to understand the material. This is followed by data warehouse topic and how different is from database concept. The notion of data mining process is explained and how it relates to the complete KDD process, as it is very important to understand that data mining is not an isolated subject. We will then overview a survey of some techniques used to implement data mining algorithms. We will follow by studying some core topics of data mining; classification ,clustering, and association rules. Other concepts, such as prediction, regression , and pattern matching, will also be covered, but viewed as special cases of the three core topics. In each concept we will only concentrate on the most popular techniques and algorithms.

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Curricular information is subject to change

Learning Outcomes:

- Why Data Mining and what is Data Mining?
- What is Data Warehouse and its architecture?
- Understand multi-dimensional data model.
- Understand the data pre-processing phase.
- Understand core functions of Data Mining.
- Classification, clustering and association rules .

Indicative Module Content:


Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities


Autonomous Student Learning






Approaches to Teaching and Learning:
The delivery of this module will consists of
- 2 lectures per week for 12 weeks
- 1 practical/tutorial session of 2 hours per week for 12 weeks. During these sessions we will go into the details of some popular algorithms and concepts that were introduced in the lectures. We will also use a data mining software tool to analyse some datasets and learn how the whole process of the data mining works, its advantages and disadvantages.
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Multiple Choice Questionnaire: short examination during the semester Varies over the Trimester n/a Graded No


Lab Report: These are submissions of tutorial or practical work that have been carried out in 2-hour tutorial/practical sessions. Varies over the Trimester n/a Graded No


Examination: End of semester exam 2 hour End of Trimester Exam No Graded No


Carry forward of passed components
Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

The students will be given feedback on their tutorial or practical work within 2 weeks following their submissions.

Name Role
Yogesh Bansal Tutor
Mr Quoc Hung Ngo Tutor
Nishma Laitonjam Tutor
Yunan Li Tutor
Mr Badri Narayanan Tutor
Mr Anjan Venkatesh Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Lecture Offering 1 Week(s) - Autumn: All Weeks Thurs 11:00 - 11:50
Lecture Offering 1 Week(s) - Autumn: All Weeks Tues 12:00 - 12:50
Practical Offering 1 Week(s) - 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12 Tues 16:00 - 17:50
Practical Offering 1 Week(s) - 6 Tues 16:00 - 17:50