Date | Topic | Readings | Assignment Out |
Sep | |||
5 | Course Overview | Ch. 1 | |
10 | Data Warehousing (DW) and OLAP | Ch. 3 | |
12 | DW and OLAP: Design and Implementation | Ch. 3,4 | |
17 | Data Preprocessing and Data Cleaning | Ch. 2 | Problem set 1 |
19 | Mining Association Rules Algorithms: Definitions and Apriori Algorithm | Ch. 5 | |
24 | Algorithms for Frequent Itemset Mining: FP-Tree, MaxMiner | Ch. 5 | |
26 | no class | ||
Oct | |||
1 | Association Mining and Correlation Analysis | Ch. 5 | Program 1: Association Rules Mining |
3 | Classification | Ch. 6 | |
9 | Scalable Classification Algorithms: SPRINT and SLIQ | Ch. 6 | |
10 | Scalable Classification Algorithms: NBC and ensemble methods | Ch. 6 | |
15 | Clustering Large Datasets: CLARANS and BIRCH | Ch. 7 | Problem Set 2 |
17 | no class | ||
22 | Clustering Large Datasets: CURE and DBSCAN | Ch. 7 | |
24 | Clustering Large Datasets: CHAMELEON, Graph based Clustering and CLIQUE | Ch. 7 | |
29 | Mid-Term | ||
31 | Clustering: EM and validation methods | Ch. 7 | Program 2: Classification |
Nov | |||
5 | Time Series Indexing | Ch. 8 | |
7 | Time Series Indexing and Mining | Ch. 8 | |
14 | Sequential Pattern Mining | ||
19 | Data Stream Mining | ||
26 | Data Stream Mining and Querying using Sketches | Problem Set 3 | |
28 | Dimensionality Reduction | Program 3: Clustering | |
Dec | |||
3 | Outlier Detection | Ch. 7 | |
5 | Web Mining and Querying | ||
10 | Spatio-temporal Data Mining | Ch. 10 |
|
12 | Wrap-Up | ||
17 | Final Exam |