Data MiningInstructor: Pedro Domingos
Textbook: Tom Mitchell, Machine Learning, McGraw-Hill,
Download Slides from here
Topics
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Lecture Notes
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| Introduction; Inductive learning, Instance-based learning | pdf, pptx |
| Decision trees; Empirical evaluation | |
| Bayesian Learning | |
| Rule Induction | |
| Neural networks | |
| Genetic algorithms, model ensembles | |
| SVMs; Learning theory | |
| Clustering | |
| Association rules | |
| Memorial Day - No Class |
Topic
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Slides
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PDF of Slides
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Notes in PS
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Notes in PDF
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| Overview of Data Mining | PPT | Postscript | ||
| Association-Rules, A-Priori Algorithm | PPT | Postscript | ||
| Other Frequent-Pair Algorithms | PPT | |||
| Correlated Items | PPT | Postscript | ||
| Query Flocks | Postscript | |||
| PageRank, Hubs-and-Authorities | PPT | Postscript | ||
| Web Mining | Postscript | |||
| Stream Mining, Part I | PPT | |||
| Stream Mining, Part II | PPT | |||
| Stream Mining, Part III | PPT | |||
| Clustering, Part I | PPT | Postscript | ||
| Clustering, Part II | PPT | Postscript | ||
| Clustering Part III --- Stream Clustering | PPT | |||
| Matching Sequences | Postscript | |||
| Mining Event Sequences | Postscript |
Professor David Mease
Lecture Powerpoint Slides and Videos:
Lecture 8 = Thursday 10/8 Video 1 Video 2 Video 3
Lecture 7 = Thursday 10/1 Video 1 Video 2 Video 3
Lecture 6 = Thursday 9/24 Video 1 Video 2 Video 3
Lecture 5 = Saturday 9/19 Video 1 Video 2 Video 3
Lecture 4 = Thursday 9/10 Video 1 Video 2 Video 3
Lecture 3 = Thursday 9/3 Video 1 Video 2 Video 3
Lecture 2 = Saturday 8/29 Video 1 Video 2 Video 3
Lecture 1 = Thursday 8/27 Video 1 Video 2 Video 3
Course Title: Data Mining
Instructor: Padhraic Smyth
Download Slides here :
Introduction to Data Mining:
- Introduction to Data Mining [PPT] [PDF]
- Measurement and Data [PPT] [PDF]
- Exploratory Data Analysis and Visualization [PPT] [PDF]
Slides
Text Mining
- text classification [PPT] [PDF]
- text mining and topic models [PPT] [PDF]
- notes on graphical models [PPT] [PDF]
Recommender Systems
Web Data Analysis
Time Series Analysis and Anomaly Detection
- review paper on hidden Markov models
- Poisson-Markov event detection paper by Ihler, Hutchins, and Smyth
Course Overview :
The Course will cover the following materials:
a) fundamentals, data mining concepts and functions, data pre-processing, data reduction, mining association rules in large databases, classification and prediction techniques, clustering analysis algorithms,data mining languages, data mining applications and new trends.
b)
Advanced Knowledge discovery in semi-structured/unstructured data
repositories with emphasis on emerging computational intelligence
paradigms such as soft computing and artificial life. Application will
be visited in special themes: advanced transactional data mining, Web
Mining, Text Mining, Bioinformatics, and other scientific and
engineering applications.
Text Book :
Data Mining: Concepts and Techniques, 1st or 2nd Ed., Jiawei Han and Micheline Kamber, Morgan Kaufmann, 2003 or 2006. ISBN 1-55860-901-6
Book Web site: http://www-faculty.cs.uiuc.edu/~hanj/bk2/index.html
Course Outline Get the PDF version of the Course Syllabus
Introduction Get Slides
1 What Motivated Data Mining? Why Is It Important?
2 So, What Is Data Mining?
3 Data Mining--On What Kind of Data?
4 Data Mining Functionalities—What Kinds of Patterns Can Be Mined?
5 Are All of the Patterns Interesting?
6 Classification of Data Mining Systems
7 Data Mining Task Primitives
8 Integration of a Data Mining System with a Database or Data Warehouse System
9 Major Issues in Data Mining
10 Data Mining Applications
11 Data Mining System Products and Research Prototypes12 Social Impacts of Data Mining
2 So, What Is Data Mining?
3 Data Mining--On What Kind of Data?
4 Data Mining Functionalities—What Kinds of Patterns Can Be Mined?
5 Are All of the Patterns Interesting?
6 Classification of Data Mining Systems
7 Data Mining Task Primitives
8 Integration of a Data Mining System with a Database or Data Warehouse System
9 Major Issues in Data Mining
10 Data Mining Applications
11 Data Mining System Products and Research Prototypes12 Social Impacts of Data Mining
Data Preprocessing Get Slides Get Math Pages File
1 Why Preprocess the Data?
2 Descriptive Data Summarization
3 Data Cleaning
4 Data Integration and Transformation
5 Data Reduction
6 Data Discretization and Concept Hierarchy Generation
7 Feature Selection Techniques
2 Descriptive Data Summarization
3 Data Cleaning
4 Data Integration and Transformation
5 Data Reduction
6 Data Discretization and Concept Hierarchy Generation
7 Feature Selection Techniques
Mining Frequent Patterns and Associations Get Slides
1 Basic Concepts and a Road Map
2 Efficient and Scalable Frequent Item set Mining Methods
3 Mining Various Kinds of Association Rules
4 Using WEKA software for finding Association Rules
2 Efficient and Scalable Frequent Item set Mining Methods
3 Mining Various Kinds of Association Rules
4 Using WEKA software for finding Association Rules
Classification and Prediction Get Slides
1 What Is Classification? What Is Prediction?
2 Issues Regarding Classification and Prediction
3 Classification by Decision Tree Induction Get More Slides
4 Bayesian Classification Get Slides
5 Rule-Based Classification Get Slides
6 Prediction
7 Accuracy and Error Measures
8 Evaluating the Accuracy of a Classifier or Predictor
9 Using WEKA software for data Classification
10 Using Oracle Data Mining Get Slides
2 Issues Regarding Classification and Prediction
3 Classification by Decision Tree Induction Get More Slides
4 Bayesian Classification Get Slides
5 Rule-Based Classification Get Slides
6 Prediction
7 Accuracy and Error Measures
8 Evaluating the Accuracy of a Classifier or Predictor
9 Using WEKA software for data Classification
10 Using Oracle Data Mining Get Slides
Classification Using Lazy Learning Techniques Get Slides
1 Tasks of concept learning and classification
2 Features of lazy learning
3 Similarity measures
4 Calculate and Explain values of similarity
5 Formulate lazy learning tasks
6 Lazy learning algorithms : (Instance-based learning and kNN-learning)
7 Apply the lazy learning algorithms to learning tasks, (Classification task)
8 Advantages and disadvantages of lazy learning algorithms
2 Features of lazy learning
3 Similarity measures
4 Calculate and Explain values of similarity
5 Formulate lazy learning tasks
6 Lazy learning algorithms : (Instance-based learning and kNN-learning)
7 Apply the lazy learning algorithms to learning tasks, (Classification task)
8 Advantages and disadvantages of lazy learning algorithms
Classification using Soft-Computing Get Slides
1 Introduction to Soft Computing
2 Introduction to Rough Set Theory
3 Reduct Computation Techniques
4 Classification using Rough Set Theory 5 Using Rosetta Tool for Reduct computation and data Classification
6 Major Issues in Rough Set Theory for Data Mining
7 Fuzzy Set and Data Mining Get Slides
2 Introduction to Rough Set Theory
3 Reduct Computation Techniques
4 Classification using Rough Set Theory 5 Using Rosetta Tool for Reduct computation and data Classification
6 Major Issues in Rough Set Theory for Data Mining
7 Fuzzy Set and Data Mining Get Slides
Cluster Analysis Get Slides Get More Slides
1 What Is Cluster Analysis?
2 Types of Data in Cluster Analysis
3 A Categorization of Major Clustering Methods
2 Types of Data in Cluster Analysis
3 A Categorization of Major Clustering Methods
Mining Spatial, Multimedia, Text, and Web Data Get Slides
1 Spatial Data Mining
2 Multimedia Data Mining
3 Text Mining Get Slides
4 Mining the World Wide Web Get Slides
2 Multimedia Data Mining
3 Text Mining Get Slides
4 Mining the World Wide Web Get Slides
Applications and Trends in Data Mining
1 Data Mining Applications
2 Data Mining System Products and Research Prototypes
3 Additional Themes on Data Mining
4 Social Impacts of Data Mining
5 Data Mining Methodologies Get Slides
2 Data Mining System Products and Research Prototypes
3 Additional Themes on Data Mining
4 Social Impacts of Data Mining
5 Data Mining Methodologies Get Slides
Data Warehouse and OLAP Technology: An Overview Get Slides
1 What Is a Data Warehouse?
2 A Multidimensional Data Model
3 Data Warehouse Architecture
4 Data Warehouse Implementation
5 From Data Warehousing to Data Mining
2 A Multidimensional Data Model
3 Data Warehouse Architecture
4 Data Warehouse Implementation
5 From Data Warehousing to Data Mining
Required Software
WEKA
is a software for machine learning and data mining . WEKA is an open
source software issued under the GNU General Public License.
Download the software from: http://www.cs.waikato.ac.nz/ml/weka/
Download the software from: http://www.cs.waikato.ac.nz/ml/weka/
Rosetta is a software for data reduction and classification purposes based on the concepts of Rough Set Theory.
Download the software from: http://rosetta.lcb.uu.se/general/
Download the software from: http://rosetta.lcb.uu.se/general/
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