Data Mining

Academic Year: 2015/2016, 1st semester
Lecturer:Mouna Kacimi
Teaching assistant:Mouna Kacimi
Lectures: Thursdays and Fridays 10:30-12:30
Labs: Thursdays14:00-16:00
Office hours:appointment by email

Lecture Notes

1- Introduction to Data Mining (pdf)
2- Getting to Know Your Data(pdf)
3- Frequent Patterns Mining (pdf)
4- Mining Sequential Patterns (pdf)
5- Supervised Learning: Decision Trees(pdf)
6- Supervised Learning: Rule-based Classification (pdf)
7- Supervised Learning: Bayesian Classifiers (pdf)
8- Supervised Learning: Evaluation of Classifiers (pdf)
9- Supervised Learning: Lazy Learners (pdf)
10- Supervised Learning: Regression Analysis (pdf)
11- Supervised Learning: Maximum Likelihood Estimation (pdf)
12- Supervised Learning: Hidden Markov Models (pdf)
13- Unsupervised Learning: Partitioning Methods (pdf)
14- Unsupervised Learning: Hierarchical Methods (pdf)
15- Unsupervised Learning: Density-based Methods (pdf). DBSCAN task (pdf)
16- Text Mining: Models and Applications (pdf)
17- Link Mining: Graphs and Networks (pdf)

Labs (Assignments)

1- Lab1: Analyzing Data (pdf). The dataset can be found here
2- Lab2: Mining Frequent Patterns from Fast food data (pdf). The dataset can be found here
3- Lab3: Classifying Tweets (pdf). The dataset can be found here
4- Lab4: Sentiment-based Classification (pdf). The training dataset can be found here
5- Lab5: Part-Of-Speech Tagging (pdf). The dataset can be found here
6- Lab6: Clustering Tweets(pdf)


1- Exercises1: Decision Trees (pdf). The dataset can be found here
2- Exercises2: Bayesian Classifiers (pdf).
3- Exercises3: Hidden Markov Models (pdf).