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Dept. of Computer Science University of Liverpool

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Presentation on theme: "Dept. of Computer Science University of Liverpool"— Presentation transcript:

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: 07/04/2019 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 07/04/2019 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 ( 07/04/2019 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: 07/04/2019 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 07/04/2019 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


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