SSST - Data Mining Techniques and Applications L5

Data Mining Techniques and Applications

Programme(s) where module is offered

BSc Computer Science with Electrical Engineering;
BSc Computer Science with Economics;
BSc Computer Science with Management;
BSc Computer Science with International Relations;
BSc Computer Science with Political Science;
BSc Information Systems with Electrical Engineering;
BSc Information Systems with Economics;
BSc Information Systems with Management;
BSc Information Systems with International Relations;
BSc Information Systems with Political Science;

Status (core, option, free choice)

Core

FHEQ Level

Unit Value

8 ECTS

Term taught

Spring

Pre-Requisite Modules or Qualifications

CSIS240 Database Systems

Module Code

CSIS355

Module coordinator

Amer Hadžikadić

Applicable From

2016

Educational Aims of the Module

The overall aim of this module is to introduce students to modern data mining techniques and their use in business and other areas of applications.
In particular, the module explores basic concepts, principles and techniques of data mining with emphasis on both the technical and the practical issues.
The module provides students with an understanding in evaluating and comparing data mining solutions for effective use of the solutions in practice.
The module also equips students with some hands-on experience and skills in conducting a data mining project using a data mining software tools.

Module Outline/Syllabus

Introduction: Concept of data mining. Data mining and KDD. Data mining process. Major data mining tasks. Data mining approaches. Overview of data mining solutions. Importance of evaluation. Evolution of data mining. Promises and challenges.
Understanding Data: data and data sets. Data types. Data quality, Data pre-processing. Data summarisation and visualisation.
Data Mining Techniques I: Problem of cluster detection. Proximity measures. Basic clustering methods: K-means and Agglomeration. Validation and evaluation of clusters. Overview of other types of clustering methods. Clustering in practice.
Data Mining Techniques II: Problem of classification. Decision tree induction approach: ID3 and other tree induction solutions. Nearest neighbour approach: kNN and PEBLS methods. Statistical approach: Naïve Bayes method. Overview of other classification approaches. Evaluation of classifiers. Problem of overfitting and solutions. Classification in practice.
Data Mining Techniques III: Problem of association rule discovery. Apriori algorithms for Boolean, generalised and quantitative association rules. Evaluation of association rules. Other types of association rules. Association rule discovery in practice.
Data Mining Projects. Data Mining project life cycle. The industry standard: CRISP-DM guideline for data mining. A case study on customer segmentation
Data Mining software tool WEKA: Overview of WEKA functions. Data Reprocessing in WEKA. WEKA visualisation facilities for data and patterns, WEKA data mining functions. WEKA evaluation parameters. Choosing the best data mining solutions in WEKA. Overview of data mining software tools.
Putting Everything Into Perspective: Database, data warehouse, query, OLAP, data mining and decision support. Data, information and Knowledge in enterprises. Application areas of data mining. Ethical and professional issues regarding data mining.

Student Engagement Hours

Type	Number per Term	Duration	Total Time
Lectures	30	2 hours	60 hours
Laboratory sessions	15	2 hours	30 hours
Total Guided/Independent Learning Hours			110
Total Contact Hours			90
Total Engagement Hours			200

Assessment Method Summary

Type	Number Required	Duration / Length	Weighting	Timing / Submission Deadline
Final exam	1	180 minutes	50%	End of semester
Mid-term exam	1	90 minutes	10%	Week 8
Project (Group)	1	2000 words	10%	Week 15
Test	3	60 minutes	30%	Weeks 5, 11 and 14

Module Outcomes

Intended Learning Outcomes:

Understand key concepts and principles of data mining
Understand state-of-art approaches and techniques in data mining and visualisation
Develop a working application using a commercial data mining/data warehousing software tool

Teaching and Learning Strategy:

Lectures introduce theoretical and conceptual materials from the recommended textbooks (ILO: 1, 2)
Laboratory sessions provide exercises to apply the theory and use data mining software tools (ILO: 2, 3)
Group project enables students to develop team-work skills and apply what they have learnt in the module to a practical problem (ILO: 3)

Assessment Strategy:

Test (ILO: 1, 2)
Mid-term exam (ILO: 1, 2)
Final exam (ILO: 1-3)
Project (ILO: 3)

Practical Skills:

Understand the particular uses of algorithms in applications
Use Weka to conduct data mining
Accurately interpret the results and argue the gained knowledge and module of action as a result of the data mining.

Teaching and Learning Strategy:

Laboratory sessions (PS: 1, 2,3)
Group project (PS: 1, 2,3)
Lectures (PS: 1,3)

Assessment Strategy:

Test (ILO: 1)
Mid-term exam (ILO: 1)
Final exam (ILO: 1)
Project (ILO: 1- 3)

Transferable Skills:

Oral and written presentation: ability to express ideas clearly and precisely
Critical thinking
Team working skills
Data analysis

Teaching and Learning Strategy:

Personal and team projects to read, evaluate and present knowledge (TS: 1, 3)
Practical classes and application on real life data (TS: 1, 2, 3, 4)

Assessment Strategy:

Project (TS: 1-4)
Test (TS: 2,4)
Mid-term exam (TS: 2,4)
Final exam (TS: 2,4)

Key Texts and/or other learning materials

Set text

Du,H., (2010) Data Mining Techniques and Applications, Cengage Learning

Supplementary Materials

Kimball, R., Ross, M., (2013), The Data Warehouse Toolkit, 3rd Edition, John Wiley & Sons
Tan, P-N., Steinbach, M., Kumar, V., (2013) Introduction to Data Mining.
The Data Warehousing Institute https://tdwi.org/Home.aspx
Dr R. Meredith: Data Warehousing Podcasts https://itunes.apple.com/gb/podcast/fit5095-data-warehousing/id306569323?mt=2
Tatroe, K., MacIntyre, P., Lerdorf, R., Programming PHP, 3rd Edition, O’Reilly Media

Please note

This specification provides a concise summary of the main features of the module and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if he/she takes full advantage of the learning opportunities that are provided.

More detailed information on the learning outcomes, content and teaching, learning and assessment methods of each module and programme can be found in the departmental or programme handbook.

The accuracy of the information contained in this document is reviewed annually by the University of Buckingham and may be checked by the Quality Assurance Agency.

Date of Production : Autumn 2016

Date approved by School Learning and Teaching Committee: 28th September 2016

Date approved by School Board of Study : 12th October 2016

Date approved by University Learning and Teaching Committee: 2nd November 2016

Date of Annual Review: December 2017

SSST Subjects

Data Mining Techniques and Applications

Programme(s) where module is offered

Status (core, option, free choice)

FHEQ Level

Unit Value

Term taught

Pre-Requisite Modules or Qualifications

Module Code

Module coordinator

Applicable From

Educational Aims of the Module

Module Outline/Syllabus

Student Engagement Hours

Assessment Method Summary

Module Outcomes

Key Texts and/or other learning materials

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