| 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 |
|
|