CSE012/CS059 – Data Mining Fall 2014

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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 (pptx, pdf) Chapters 1,2 from the book “Introduction to Data Mining” by Tan Steinbach Kumar. Lecture 2: Probability Theory. Data, pre-processing and post-processing (ppt, pdf) Introduction to Probability Theory (thanks to Eli Upfal and Aris Anagnostopoulos) 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: Finding frequent Itemsets. The A-priori algorithm. Finding Association Rules (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. (ppt, pdf) Chapter 3 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. Lecture 6: Finding Similar Pairs. Min-Hashing. Locality Sensitive Hashing. (ppt, pdf) Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman, Jure Leskovec. Lecture 7: Dimensionality Reduction. Singular Value Decomposition (SVD). Principal Component Analysis (PCA). (ppt, 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. Lecture 8: Clustering. The k-means algorithm. Hierarchical Clustering. The DBSCAN algorithm. (ppt, 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 and the EM algorithm. Clustering Evaluation. Sequence Segmentation. (ppt, 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) . Lecture 10: Minimum Description Length (MDL). Introduction to Information Theory. Co-Clustering. (ppt, pdf) Some information about MDL and Information Theory appears in Chapters 2,4 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar Deepayan Chakrabarti, Spiros Papadimitriou, Dharmendra Modha, Christos Faloutsos, Fully Automatic Cross-Associations, KDD 2004, Seattle, August 2004. [PDF] Lecture 11: Classification. Decision Trees. Evaluation. (ppt, pdf) Chapter 4 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar Lecture 12: Classification. Nearest Neighbor Classification. Support Vector Machines. Logistic Regression Classification. Naive Bayes Learning. Supervised Learning. (ppt, pdf) Chapters 4,5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar Chapter 12 from the book Mining Massive Datasets of Anand Rajaraman and Jeff Ullman, Jure Leskovec. Chapter 13 from the book "Introduction to Information Retrieval" by C. Manning, P. Raghavan, H. Schutze Lecture 13: Link Analysis for Web 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 14: Absorbing Random Walks. Coverage Problems. (ppt, pdf) P. G. Doyle, J. L. Snell. Random Walks and Electrical Networks. Lecture 15: The Map-Reduce computational paradigm (ppt, pdf) Chapter 2 from the book Mining Massive Datasets of Anand Rajaraman and Jeff Ullman, Jure Leskovec.