CS059 – Data Mining Fall 2012

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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 and Jeff Ullman, Evimaria Terzi, 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 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. Lecture 4: Frequent Itemests, Association Rules. Evaluation. Beyond Apriori (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. Lecture 5: Similarity and Distance. Metrics. Min-wise independent hashing. (ppt,pdf) Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman. Chapter 2 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Lecture 6: Min-wise independent hashing. Locality Sensitive Hashing. Clustering, K-means algorithm (ppt,pdf) Chapter 3 from the book Mining Massive Datasets by Anand Rajaraman and Jeff Ullman. Chapter 8 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Lecture 7: Hierarchical clustering, DBSCAN, Mixture models and the EM algorithm  (ppt,pdf) Chapter 8,9 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Lecture 8a: Clustering Validity, 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 8b: Clustering Validity, Minimum Description Length (MDL), Introduction to Information Theory, Co-clustering using MDL. (ppt,pdf) Chapter 2, Evimaria Terzi, Problems and Algorithms for Sequence Segmentations, Ph.D. Thesis (PDF)  Lecture 9: Dimensionality Reduction, Singular Value Decomposition (SVD), Principal Component Analysis (PCA). (ppt,pdf) Appendices A, B from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Lecture 10a: Classification. Decision Trees. Evaluation. (ppt,pdf) Chapter 4 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Lecture 10b: Classification. k-Nearest Neighbor classifier, Logistic Regression, Support Vector Machines (SVM), Naive Bayes (ppt,pdf) Chapters 4,5 from the book “Introduction to Data Mining” by Tan, Steinbach, Kumar. Lecture 11: Naive Bayes classifier. Supervised Learning. Web Search and PageRank (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, HITS, Random Walks  (ppt,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" by D. Easley, J. Kleinberg Lecture 13: Absorbing Random Walks. Coverage Problems (Set Cover, Maximum Coverage)  (ppt,pdf)