CSE012/CS059 – Data Mining

Spring 2017

Lecture Slides

For the slides of this course we will use slides and material from other courses and books. We thank in advance: Tan, Steinbach and Kumar, Anand Rajaraman Jeff Ullman, and Jure Leskovec, Evimaria Terzi, Aris Anagnostopoulos for the material of their slides that we have used in this course.

Introduction: Logistics (in Greek) (pptx, pdf)

Lecture 1: Introduction to Data Mining (pptx, pdf)

Chapters 1,2 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Article: Data Scientist: The Sexiest Job of the 21st Century.

Tutorial 1: Introduction to discrete probabilities. (pdf)

Thanks to Aris Anagnostopoulos for the slides.

Lecture 2: What is data? The data mining pipeline. Preprocessing and postprocessing. Samping and normalization (pptx, pdf)

Chapters 2,3 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Chapter 1 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.

Lecture 3: Frequent Itemsets and Association Rules (pptx, pdf)

Chapter 6 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Chapter 6 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.

Tutorial 2: Introduction to Python. (pptx, pdf, ipynb, html)

The file with the image here

Lecture 4: Similarity and Distance. Recommendation Systems (pptx, pdf)

Chapter 2 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Chapters 3,9 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.
Article: The long tail.

Tutorial 3: Introduction to Pandas. (pptx, pdf, ipynb, html)

The distributions_short.csv file.

Lecture 5: Finding similar pairs. Min-hash signatures. Locality Sensitive Hashing (pptx, pdf)

Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.

Lecture 6: Dimensionality Reduction. Singular Value Decomposition (SVD). Principal Component Analysis (PCA). (pptx, pdf)

Chapter 11 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.
Appendices A,B from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
A tutorial on Principal Component Analysis, Jonathon Shlens

Tutorial 4: Introduction to Numpy, Scipy, SciKit for handling matrices. (ipynb, html)

Lecture 7: Clustering. The k-means algorithm. Hierarchical Clustering. The DBSCAN algorithm. Clustering Evaluation (pptx, pdf)

Chapter 8 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Chapter 7 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.

Lecture 9: Mixture Models. The EM Algorithm. Sequence Segmentation (pptx, pdf)

Chapters 8,9 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Notes on the EM algorithm by Aris Anagnostopoulos, University of Rome La Sapienza
Chapter 2, Evimaria Terzi, Problems and Algorithms for Sequence Segmentations, Ph.D. Thesis (PDF) .

Tutorial 5: Introduction to Clustering and Feature Extraction with SciKit-Learn. (ipynb, html)

Lecture 9: Classification. Decision Trees. Evaluation. (pptx, pdf)

Chapter 4 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.

Lecture 10: Other classification techniques. Nearest Neighbor Classifiers, Support Vector Machines, Logistic Regression, Naive Bayes Classification. Supervised Learning. (pptx, pdf)

Chapters 4,5 from the book “Introduction to Data Mining” by Tan Steinbach Kumar.
Chapter 12 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec.
Chapter 13 from the book "Introduction to Information Retrieval" by C. Manning, P. Raghavan, H. Schutze

Tutorial 6: Introduction to Classification with SciKit. (ipynb,html)

Lecture 11: Link Analysis Ranking Web Ranking. PageRank, Random Walks, HITS. Absorbing Random Walks. (pptx, pdf)

Chapter 21 from the book "Introduction to Information Retrieval" by C. Manning, P. Raghavan, H. Schutze
Chapter 14 from the book "Networks Crowds and Markets" of D. Easley and J. Kleinberg
P. G. Doyle and J. L. Snell. Absorbing Random Walks and Electrical Networks.

Lecture 12: Community discovery in graphs. Edge Betweenness Centrality. (pptx, pdf)

Chapter 3 from the book "Networks Crowds and Markets" of D. Easley and J. Kleinberg

Tutorial 7: Introduction to Network analysis with NetworkX. (ipynb, html)

The files graph_edges.txt and karate.gml

Lecture 13: Absorbing Random Walks. Coverage Problems. (pptx, pdf)

Chapter 2 from the book "Approximation Algorithms" of V. Vazirani
Notes on the approximation ratio of the Greedy algorithm by Debmalya Panigrahi and Chandra Chekuri.