1. Dept. of Computer Science University of Liverpool
COMP527: Data Mining
COMP527:
Data Mining
M. Sulaiman Khan
Dept. of Computer Science
University of Liverpool
2009
07/04/2019
Introduction to the course

2. Introduction to the course
COMP527: Data Mining
COMP527:
Data Mining
Introduction to the Course
Introduction to Data Mining
Introduction to Text Mining
General Data Mining Issues
Data Warehousing
Classification: Challenges, Basics
Classification: Rules
Classification: Trees
Classification: Trees 2
Classification: Bayes
Classification: Neural Networks
Classification: SVM
Classification: Evaluation
Classification: Evaluation 2
Regression, Prediction
Input Preprocessing
Attribute Selection
Association Rule Mining
ARM: A Priori and Data Structures
ARM: Improvements
ARM: Advanced Techniques
Clustering: Challenges, Basics
Clustering: Agglomerative/Divisive
Clustering: Advanced Algorithms
Hybrid Approaches
Graph Mining, Web Mining
Text Mining: Challenges, Basics
Text Mining: Text-as-Data
Text Mining: Text-as-Language
Revision for Exam
07/04/2019
Introduction to the course

3. Introduction to the course
Today's Topics
COMP527:
Data Mining
Me, You: Introductions
Lectures
Tutorials
References
Course Summary
Assessment
What you will learn
Conclusion
07/04/2019
Introduction to the course

4. Introduction to the course
Introductions
COMP527:
Data Mining
Name: M. Sulaiman Khan
Web:
Hours: Tuesday: am
Wednesday: pm
Thursday: am
Information Science:
Information Retrieval, Data Mining, Association Rule Mining, XML, Databases, Interoperability, Parallel Processing...
07/04/2019
Introduction to the course

5. Introduction to the course
You!
COMP527:
Data Mining
...
07/04/2019
Introduction to the course

6. Introduction to the course
Lectures
COMP527:
Data Mining
Lecture Slots:
Tuesday: am Here
Wednesday: pm Here
Thursday: am Here
Course requirement: hours of lectures
Semester Timetable:
8 weeks class, 3 weeks easter, 4 weeks class.
07/04/2019
Introduction to the course

7. Introduction to the course
Tutorials/Lab Sessions
COMP527:
Data Mining
Location:
To be confirmed
Aims:
Provide time for practical experience
Answer any questions from lectures/reading
Informal self-assessment exercises
Software:
Data mining 'workbench' software WEKA. Freely downloadable from University of Waikato:
JPVM – Java Parallel Virtual Machine (Parallel Programming on distributed PCs using Java)
07/04/2019
Introduction to the course

8. Introduction to the course
Course Web Sites
COMP527:
Data Mining
Departmental Home Page:
Lecture Notes, Assignments, Exercises:
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Introduction to the course

9. Introduction to the course
Reference Texts
COMP527:
Data Mining
Witten, Ian and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition, Morgan Kaufmann, 2005
Dunham, Margaret H, Data Mining: Introductory and Advanced Topics, Prentice Hall, 2003
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Introduction to the course

10. Introduction to the course
Frequently Used Resources
COMP527:
Data Mining
Han and Kamber, Data Mining: Concepts and Techniques, Second Edition, Morgan Kaufmann, 2006
Berry, Browne, Lecture Notes in Data Mining, World Scientific, 2006
Berry and Linoff, Data Mining Techniques, Second Edition, Wiley, 2004
Zhang, Association Rule Mining, Springer, 2002
Konchady, Text Mining Application Programming, Thomson, 2006
Weiss et al., Text Mining: Predictive Methods for Analyzing Unstructured Information, Springer, 2005
Inmon, Building the Data Warehouse, Wiley, 1993
KDD (
PAKDD (
PKDD (
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Introduction to the course

11. Introduction to the course
Frequently Used Websites
COMP527:
Data Mining
CiteSeer:
KDNuggets:
UCI Repository: (plus follow link to Machine Learning Archive)‏
Wikipedia:
MathWorld:
Google Scholar:
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Introduction to the course

12. Introduction to the course
Course Summary
COMP527:
Data Mining
Introduction, Basics: lectures
Data Warehousing: lecture
Classification: lectures
Input Preprocessing: lectures
Association Rule Mining lectures
Clustering: lectures
Hybrid Approaches: lecture
Graph Mining: lecture
Text Mining: lectures
Revision: lecture
Total: 30 lectures
07/04/2019
Introduction to the course

13. Introduction to the course
Assessment
COMP527:
Data Mining
75% End of Year Exam:
2 ½ hours
Short Answer and/or Essays
Choose 4 of 5 sections
25% Continuous Assessment:
12% Assignment 1 (Due :00:00)‏
13% Assignment 2 (Due :00:00)
Self assessment exercises
Weekly (or as desired) during tutorial session
07/04/2019
Introduction to the course

14. Introduction to the course
What you will learn:
COMP527:
Data Mining
Data Engineering principles: Theory, Systems, Data and Technologies
Modelling and analysis of heterogenic data sources
Data Mining Techniques for Intelligent Data analysis and modelling
Knowledge discovery in Databases (KDD)
Distributed pattern mining
Applications of Data Mining algorithms
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Introduction to the course

15. Introduction to the course
Conclusion
COMP527:
Data Mining
Data Mining is an exciting, new and applied technology
Almost all DBMS have some intelligence module in their design
Work hard!
Do some programming and implement some algorithms outside class
Think about your project/dissertation and what you want to get out of the course beyond the lectures
07/04/2019
Introduction to the course