10/24

  10/26

Lecture 1: Introduction and Fast Similarity Search

• kd-trees and Locality Sensitive Hashing

Reading:

• Multidimensional Binary Search Trees Used for Associative Searching,
  J.L. Bentley. CACM, 18,9, Sept. 1975
• Mining of Massive Datasets Chapter 3

  10/31

  11/2

Lecture 2: Frequent Itemsets and Association Rules

• Market-basket analysis and the Apriori algorithm
• Handling large datasets with limited-RAM and limited-pass algorithms

Reading:

• Mining of Massive Datasets Chapter 6

  11/7

  11/9

Lecture 3: Data Decomposition

• Singular Value Decomposition (SVD)
• SVD applications, case studies
• CUR for sparse decomposition

Reading:

• Mining of Massive Datasets Chapter 11
• (Optional) Tutorial on Principal Component Analysis (PCA), J. Schlens.
• (Optional) Quantifiable data mining using ratio rules, F. Korn; A. Labrinidis; Y. Kotidis; C. Faloutsos. VLDB Journal 8(3-4): 254-266, 2000

  11/14

  11/16

Lecture 4: Clustering

• Distance measures
• Hierarchical clustering, k-means, BFR, CURE algorithms

Reading:

• Mining of Massive Datasets Chapter 7

  11/16

  11/21

Lecture 5: Outlier Mining

• Extreme-value analysis
• Density-based outlier detection
• Ensemble methods

Reading:

• Isolation Forest, F. T. Liu, K. M. Ting, Z-H. Zhou. ICDM 2008.
• (Optional) LOF: Identifying density-based local outliers, M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander. SIGMOD, pages 93–104, 2000.
• (Optional) LOCI: Fast outlier detection using the local correlation integral, S. Papadimitriou, H. Kitagawa, P. B. Gibbons, and C. Faloutsos. ICDE, 2003.

  11/23

No Class: Thanksgiving

  11/28
  11/30

Lecture 6: Graphs: Link Analysis

• Ranking nodes in a graph
• Random walks (with restart), Pagerank, Topic-sensitive Pagerank, HITS

Reading:

• Mining of Massive Datasets Chapter 5
• (Optional) Anatomy of a Large-Scale Hypertextual Web Search Engine, S. Brin and L. Page. 7th Intl World Wide Web Conf, 1998.
• (Optional) Authoritative sources in a hyperlinked environment, J. Kleinberg. ACM-SIAM Symposium on Discrete Algorithms, 1998.
• (Optional) Fast Random Walk with Restart and Its Application, H. Tong, C. Faloutsos, and J.-Y. Pan. ICDM, 2006.

  11/30
  12/5

Lecture 7: Text Mining

• Topic modeling with LDA and visualization

Reading:

• Probabilistic topic models, D. Blei. Communications of the ACM, 55(4):7784, 2012.
• (Optional) Latent Dirichlet Allocation, D. Blei, A. Ng, M. Jordan. NIPS, 2002.
• (Optional) Manifold Learning for Jointly Modeling Topic and Visualization, T. M. V. Le and H. W. Lauw. AAAI, 2014.

  12/7

Lecture 8: Data Streams

• Uniform-sampling: Reservoir sampling
• Filtering: the Bloom filter
• Counting distinct elements: Flajolet-Martin algorithm
• Counting frequencies: Count-min sketch

Reading:

• Mining of Massive Datasets Chapter 4
• (Optional) Data Mining, The Textbook Chapter 12