CS059 – Data Mining Fall 2013

Home Material Slides Assignments

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. Lecture 1: Introduction to Data Mining (ppt, pdf) Chapters 1,2 from the book “Introduction to Data Mining” by Tan Steinbach Kumar. Lecture 2: Data, pre-processing and post-processing (ppt, 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, Association Rules, Apriori algorithm.(ppt, 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. Lecture 4: Association Rules, Evaluation of Rules. Alternative algorithms for frequent itemsets. (ppt, 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. Lecture 5: Similarity and Distance. Metrics. Recommender Systems. Document Shingling. (ppt, pdf) Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec. Chapter 2 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Chapter 9 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec. Lecture 6: Document Shingling. Min-hashing and Sketching. Locality Sensitive Hashing (LSH). (ppt,pdf) Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec. Lecture 7: Clustering: k-means, hierarchical clustering, DBSCAN.(ppt, pdf) Chapter 8 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Lecture 8: Clustering: EM Algorithm, Clustering Evaluation, Sequence Segmentation.(ppt, pdf) Chapters 8,9 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Course Notes on EM from Aris Anagnostopoulos, University of Rome La Sapienza. Chapter 2, Evimaria Terzi, Problems and Algorithms for Sequence Segmentations, Ph.D. Thesis (PDF) Lecture 9: Minimum Description Length (MDL). Introduction to Information Theory. Co-clustering using MDL.(ppt, pdf) Deepayan Chakrabarti, Spiros Papadimitriou, Dharmendra Modha, Christos Faloutsos, Fully Automatic Cross-Associations, KDD 2004, Seattle, August 2004. [PDF] Some details about MDL and Information Theory can be found in the book “Introduction to Data Mining” by Tan, Steinbach, Kumar (chapters 2,4). Lecture 10: Introduction to Classification. Decision Trees. Classification Evaluation. Nearest Neighbor Classifier.(ppt, pdf) Chapters 4,5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Lecture 11: Classification: Support Vector Machines, Logistic Regression, Naive Bayes Classifier. Supervised Learning. (ppt, pdf) Chapter 5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Chapter 13 from the book "Introduction to Information Retrieval" by C. Manning, P. Raghavan, H. Schutze Lecture 12: Link Analysis Ranking: PageRank -- Random Walks. The HITS algorithm. (ppt, pdf) Chapter 14 from the book “Networks Crowds and Markets” by D. Easley, J. Kleinberg. Chapter 21 from the book "Introduction to Information Retrieval" by C. Manning, P. Raghavan, H. Schutze Lecture 13: Absorbing Random Walks. Coverage. (ppt, pdf) P. G. Doyle, J. L. Snell. Random Walks and Electrical Networks.