TICS411 - Data Mining

Course Overview

01 – Introduction to Data Mining

02 – Data types, data quality, preliminary concepts

03 – Data pre-processing: features, similarities, anomalies, overfitting, validation, evaluation, hyper-parameters

04 – Unsupervised learning: crisp, probabilistic and fuzzy clustering methods (KM, FCM, SOM, DBSCAN, …)

05 – Supervised learning: rule-based classifiers, nearest neighbor, naive Bayes, logistic regression, neural networks, SVM, ...

06 – Ensemble learning: bagging, boosting, random forest, ...

07 – Potential topics: PCA, recursion, auto-encoders, online learning, etc.

Objectives of the Course

01 – Introduce foundations and concepts of data mining and machine learning

02 – Students should become familiar with traditional unsupervised and supervised data mining frameworks

03 – Discuss issues on data pre-processing, parameter optimization, and model generalization and validation – “best practices”

04 – Be actively involved in two hands-on projects

05 – Solve problems in the context of data-driven modeling for classification and prediction using Python or R

Approximated Timeline

Week 1: Introduction to Data Mining

Week 2: Issues on data pre-processing and attribute selection

Week 3: Exploratory data analysis. Introduction to unsupervised learning

Week 4: Partition-based clustering: K-Means

Week 5: Hard density-based clustering: DBSCAN

Week 6: Hierarchical agglomerative clustering. Validation indices. Topics: Fuzzy C-Means, Mountain and Subtractive clustering

Week 7: Midterm Test (T1)

Week 8: Introduction to supervised learning (predictive modeling)

Week 9: K-Nearest Neighbors, and Naive Bayes classifier

Week 10: Decision Trees

Week 11: Ensemble Methods

Week 12: Multi-Layer Perceptron neural network

Week 13: Logistic Regression or Support Vector Machines

Week 14: Synthesis of the predictive models. Questions and answers to the final test

Week 15: Final Test (T2)

Week 16: Examen (E)

Main Textbooks

[1] Pang-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar. Introduction to Data Mining. 2nd edition, Pearson, 2019

[2] Charu Aggarwal. Data Mining: The Textbook. Springer, 2015

Other Textbooks

[3] Cristopher Bishop, Pattern Recognition and Machine Learning. Springer, 2006

Datasets for Downloading

Kaggle (Link), UCI Machine Learning Repository (Link), Google Dataset Search (Link)

Supporting Material

Class Notes (available below)

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