Download Module Advanced Data Mining

H9ADM: Advanced Data
Mining
Long Title:
Advanced Data Mining
Language of Instruction:
English
Module Code:
H9ADM
Credits:
10
NFQ Level:
LEVEL 9
Field of Study:
Software and applications development and analysis
Module Delivered in
2 programme(s)
Module Coordinator:
Simon Caton
Module editor:
Margarete Silva
Teaching and Learning
Strategy:
The learning strategy involves the use of lectures, tutorials, case studies, paper reviews and practical work
as appropriate. Lectures will include active learning components such as paired discussion, problem
solving, and class feedback. Practical sessions will comprise of group work and individual learning. Learners
will also have access to data analytics research documents and publications as required.
Learning Environment:
Learning will take place in classroom or lab environments as appropriate. In lab environments, each student
will have access to IT resources. Learners will have access to library resources and to faculty outside of the
classroom where required. Module materials
Module Description:
The aim of this module is to enable learners to critically analyse the data mining process and to provide an
in-depth coverage of advanced data mining concepts and techniques. Furthermore, learners will apply these
advanced techniques in a practical context utilising appropriate toolsets for implementing data mining
activities. The module also focuses on current research in the field and enables learners to review and
critically assess this research.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1
Critically analyse data mining and knowledge discovery methodologies in order to assess best practice guidance when
applied to data mining problems in specific contexts
LO2
Investigate and evaluate key concepts and advanced data mining techniques and assess when to apply such techniques in
practical situations
LO3
Contextualise, research and utilise current data mining approaches, applications and technologies in order to provide
strategies to address processing of datasets with a variety of characteristics
LO4
Critically review current data mining research and assess research methods applied in the field
Pre-requisite learning
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if
you have not acquired the learning specified in this section.
No requirements listed
H9ADM: Advanced Data
Mining
Module Content & Assessment
Indicative Content
vThe Data Mining Process
• What is Data Mining? • Statistical Limits on Data Mining • Data Mining Methodologies (e.g., CRISP, SEMMA) • Comparison of Data Mining
Methodologies
Association Rules
• Frequent Itemsets and The Market-Basket Model • Association Rules Algorithms (e.g., APRIORI, FP-Growth) • Correlation Analysis
Classification & Prediction
• Predictive Modelling • Classification Schemes (e.g., Decision Tree Induction, Bayesian, Rule-Based) • Fuzzy Sets and Rough Sets •
Support Vector Machines • Genetic Algorithm Support to Data Mining • Classifier and Predictor Accuracy • Recommender Systems •
Machine Learning Toolsets (e.g, Weka, RapidMiner)
Cluster Analysis in Data Mining
• Types of Data in Cluster Analysis • Clustering Methods (e.g., Partitioning Methods, Hierarchical Methods) • Outlier Analysis • Evolution
Analysis
Text Processing
• Document Collections • Data Preparation • Information Retrieval models • Natural Language Processing
Stream and Sequence Data Mining
• Mining Data Streams • Mining Time-Series Data
Big Data Mining and Analytics
• Massive Datasets • Data Reduction and Normalization • Distributed File Systems • Map-Reduce Patterns and Algorithms • Data Mining
applications
Assessment Breakdown
%
Coursework
60.00%
End of Module Assessment
40.00%
Full Time
Coursework
Assessment Type
Assessment Description
Outcome
addressed
% of
total
Assessment
Date
Continuous
Assessment
(0200)
Learners may be assessed through preparation and review of
research papers and case studies. Additionally, learners may be
assessed through discussions and activities during tutorial based
sessions.
4
20.00
n/a
Project
Project work will be group based.
3
40.00
n/a
End of Module Assessment
Assessment Type
Assessment Description
Outcome
addressed
% of
total
Assessment Date
Terminal Exam
End-of-Semester Final Examination
1,2
40.00
End-of-Semester
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a
coursework element.
Reassessment Description
Learners who fail this module will be required to sit a repeat module assessment where all learning outcomes will be examined.
NCIRL reserves the right to alter the nature and timings of assessment
H9ADM: Advanced Data
Mining
Module Workload
Workload: Full Time
Workload Type
Workload Description
Hours
Frequency
Average
Weekly
Learner
Workload
Lecture
No Description
2
Every
Week
2.00
Tutorial
No Description
2
Every
Week
2.00
Independent Learning
No Description
17
Every
Week
17.00
Total Hours
21.00
Total Weekly Learner Workload
21.00
Total Weekly Contact Hours
4.00
Workload: Part Time
Workload Type
Workload Description
Hours
Frequency
Average
Weekly
Learner
Workload
Lecture
No Description
2
Every
Week
2.00
Tutorial
No Description
2
Every
Week
2.00
Independent Learning
No Description
17
Every
Week
17.00
Total Hours
21.00
Total Weekly Learner Workload
21.00
Total Weekly Contact Hours
4.00
Module Resources
Recommended Book Resources
Ian H. Witten, Eibe Frank, Mark A. Hall 2011, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition,
Morgan Kaufmann [ISBN: 978-0123748560]
Anand Rajaraman, Jeffrey David Ullman 2011, Mining of Massive Datasets, Cambridge University Press [ISBN: 978-1107015357]
John D. Kelleher, Brian Mac Namee, and Aoife D'Arcy, The book is Fundamentals of Machine Learning for Predictive Data
Analytics: Algorithms, Worked Examples, and Case Studies, MIT Press [ISBN: 0262029448]
Supplementary Book Resources
Yaser S. Abu-Mostafa, Malik Magdon-Ismail, Hsuan-Tien Lin 2012, Learning From Data, AMLBook [ISBN: 978-1600490064]
David L. Olson, Dursun Delen 2008, Advanced data mining techniques, Springer Berlin [ISBN: 978-3540769163]
Graham Williams 2011, Data Mining with Rattle and R: The Art of Excavating Data for Knowledge Discovery, Springer [ISBN: 9781441998897]
David Barber 2012, Bayesian Reasoning and Machine Learning, Cambridge University Press [ISBN: 978-0521518147]
Nisbet, R., Elder J. and Miner G. 2009, Handbook of Statistical Analysis and Data Mining Applications, Elsevier Burlington [ISBN:
978-0123747655]
Jiawei Han, Micheline Kamber, Jian Pei, Data Mining: Concepts and Techniques, 3rd Edition Ed., Morgan Kaufmann [ISBN: 9780123814791]
This module does not have any article/paper resources
Other Resources
Website: ACM Transactions on Knowledge Discovery from Data
http://tkdd.acm.org/
Website: Data Mining and
http://link.springer.com/journal/10618
Website: IEEE Transactions on Knowledge and Data Engineering
https://ieeexplore.ieee.org/xpl/RecentIs sue.jsp?punumber=69
Website: Rajaraman A., Ullman J., Mining of Massive Datasets, Cambridge
http://infolab.stanford.edu/~ullman/mmds .html
Module Delivered in
Programme Code
Programme
Semester
Delivery
MSCDA
MSc in Data Analytics
2
Core Subject
PGDDA
Post Graduate Diploma in Science in Data Analytics
2
Core Subject