1. SEG 4630 E-Commerce Data Mining — Final Review —
Hong Cheng
SEEM
Chinese University of Hong Kong
November 21, 2018
E-Commerce Data Mining

2. E-Commerce Data Mining
Final Time/Location
Time: 9:30-11:30 am Dec. 15, Tuesday
Location: 103 John Fulton Center
Coverage: Chaps 2, 4-8
You can bring two A4 size, double-sided cheat sheet
Calculator IS needed.
November 21, 2018
E-Commerce Data Mining

3. E-Commerce Data Mining
Chapter 2 (1)
Calculate data distribution
Mean, median, variance and standard deviation
Calculate distance between data objects
Minkowski distance
Distance between binary variables: symmetric and asymmetric
Cosine similarity
November 21, 2018
E-Commerce Data Mining

4. E-Commerce Data Mining
Chapter 2 (2)
Data normalization
Min-max normalization
Z-score normalization
Decimal scaling
Data reduction
Dimensionality reduction methods
Sampling
November 21, 2018
E-Commerce Data Mining

5. E-Commerce Data Mining
Chapters 4-5 (1)
Decision tree
Calculate information gain, gini index, gain ratio
Bayes theorem and Naïve Bayesian
Calculate probabilities from training datasets
Lazy classifier and k-nearest neighbor
Calculate based on different k values and different distance measures
Differences between eager and lazy classifiers
November 21, 2018
E-Commerce Data Mining

6. E-Commerce Data Mining
Chapters 4-5 (2)
Accuracy and error measures
Training error vs. validation error
Confusion matrix
ROC curve
True positive rate (TPR) and false positive rate (FPR)
Area under curve (AUC)
Evaluation methods
Hold out
Cross validation
Ensemble, bagging: know the principle
November 21, 2018
E-Commerce Data Mining

7. E-Commerce Data Mining
Chapters 6-7 (1)
Frequent patterns and association rules
Support, confidence
Generate association rules from frequent itemsets
Apriori algorithm
Candidate generation and test
Self joining
Pruning
Database scan
FPgrowth algorithm
Build FP-tree
Extract conditional DB
November 21, 2018
E-Commerce Data Mining

8. E-Commerce Data Mining
Chapter 6-7 (2)
Closed itemsets and maximal itemsets
Lift/Interest measure
Constraints
Monotonic
Antimonotonic
Convertible constraints
Sequence pattern mining: know the principle
Max-gap
min-gap
Max-span
November 21, 2018
E-Commerce Data Mining

9. E-Commerce Data Mining
Chapter 8
K-means clustering
Algorithm and calculation
Advantages and disadvantages
Hierarchical clustering: MIN, MAX, Group average
Step-wise calculation
Update distance matrix
Density-based clustering
Know the principle
Evaluating clustering quality
SSE, silhouette, entropy, purity
November 21, 2018
E-Commerce Data Mining

10. E-Commerce Data Mining
Questions?
November 21, 2018
E-Commerce Data Mining