CS3DM16-Data Mining

Module Provider: School of Mathematical, Physical and Computational Sciences
Number of credits: 10 [5 ECTS credits]
Terms in which taught: Spring term module
Non-modular pre-requisites:
Modules excluded:
Module version for: 2016/7

Module Convenor: Dr Giuseppe Di Fatta

Email: g.difatta@reading.ac.uk

Summary module description:

Automated data collection tools and mature database technology lead to tremendous amounts of data stored in databases, data warehouses and other information repositories. In this context, automated data analysis techniques (Data Mining) are becoming essential components to any information system. Application areas of these techniques include scientific computing, intelligent business, direct marketing, customer relationship management, market segmentation, store shelf management, data warehouse management, fraud detection in e-commerce and in credit card transactions etc.
This module introduces concepts, techniques and algorithms for the extraction of interesting knowledge (rules, regularities, patterns) from large data sets. The techniques span from statistics to machine learning and information science methods to generate descriptive and predictive data models.

Assessable learning outcomes:
Students are expected to understand the general Data Mining principles and techniques, and to be able to apply them in different contexts. As a Final Project they will implement and demonstrate some data mining techniques chosen from the ones presented in the module.

Additional outcomes:
Students will become familiar with the potential applications of data mining techniques in different domains. They will also learn how to carry out experimental tests for algorithm performance evaluations.

Outline content:
Introduction to Data Mining
Data preprocessing
Proximity measures
Regression, Classification and model evaluation
Clustering and cluster validity
Decision Tree Induction
Association Rule Mining
Data Workflow Management Systems

Brief description of teaching and learning methods:
The module comprises 2 lectures per week which introduce the basic algorithms used in Data Mining methods. A Final Project allows the students to apply the concepts to a practical case.

Contact hours:
  Autumn Spring Summer
Lectures 20
Guided independent study 80
Total hours by term 100.00
Total hours for module 100.00

Summative Assessment Methods:
Method Percentage
Written exam 50
Set exercise 50

Other information on summative assessment:

Formative assessment methods:

Penalties for late submission:
The Module Convenor will apply the following penalties for work submitted late, in accordance with the University policy.

  • where the piece of work is submitted up to one calendar week after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for the piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of five working days;
  • where the piece of work is submitted more than five working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

  • The University policy statement on penalties for late submission can be found at: http://www.reading.ac.uk/web/FILES/qualitysupport/penaltiesforlatesubmission.pdf
    You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

    Length of examination:
    One 90-mins examination paper in May/June.

    Requirements for a pass:
    A mark of 40% overall

    Reassessment arrangements:
    Examination only.
    One 90-mins examination paper in August/September.

    Additional Costs (specified where applicable):
    1) Required text books:
    2) Specialist equipment or materials:
    3) Specialist clothing, footwear or headgear:
    4) Printing and binding:
    5) Computers and devices with a particular specification:
    6) Travel, accommodation and subsistence:

    Last updated: 21 December 2016

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