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SEG 4630
E-Commerce Data Mining
— Final Review —
Hong Cheng
SEEM
Chinese University of Hong Kong
www.se.cuhk.edu.hk/~hcheng
May 22, 2017
E-Commerce Data Mining
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Final Time/Location
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Time: 9:30-11:30 am Dec. 15, Tuesday
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Location: 103 John Fulton Center
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Coverage: Chaps 2, 4-8
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You can bring two A4 size, double-sided cheat
sheet
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Calculator IS needed.
May 22, 2017
E-Commerce Data Mining
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Chapter 2 (1)
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Calculate data distribution
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Mean, median, variance and standard deviation
Calculate distance between data objects
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May 22, 2017
Minkowski distance
Distance between binary variables: symmetric and
asymmetric
Cosine similarity
E-Commerce Data Mining
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Chapter 2 (2)
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Data normalization
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Min-max normalization
Z-score normalization
Decimal scaling
Data reduction
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Dimensionality reduction methods
Sampling
E-Commerce Data Mining
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Chapters 4-5 (1)
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Decision tree
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Bayes theorem and Naïve Bayesian
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Calculate probabilities from training datasets
Lazy classifier and k-nearest neighbor
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Calculate information gain, gini index, gain ratio
Calculate based on different k values and different
distance measures
Differences between eager and lazy classifiers
May 22, 2017
E-Commerce Data Mining
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Chapters 4-5 (2)
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Accuracy and error measures
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ROC curve
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True positive rate (TPR) and false positive rate (FPR)
Area under curve (AUC)
Evaluation methods
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Training error vs. validation error
Confusion matrix
Hold out
Cross validation
Ensemble, bagging: know the principle
May 22, 2017
E-Commerce Data Mining
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Chapters 6-7 (1)
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Frequent patterns and association rules
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Apriori algorithm
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Support, confidence
Generate association rules from frequent itemsets
Candidate generation and test
Self joining
Pruning
Database scan
FPgrowth algorithm
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Build FP-tree
Extract conditional DB
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Chapter 6-7 (2)
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Closed itemsets and maximal itemsets
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Lift/Interest measure
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Constraints
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Monotonic
Antimonotonic
Convertible constraints
Sequence pattern mining: know the principle
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May 22, 2017
Max-gap
min-gap
Max-span
E-Commerce Data Mining
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Chapter 8
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K-means clustering
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Hierarchical clustering: MIN, MAX, Group average
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Step-wise calculation
Update distance matrix
Advantages and disadvantages
Density-based clustering
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Algorithm and calculation
Advantages and disadvantages
Know the principle
Evaluating clustering quality
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May 22, 2017
SSE, silhouette, entropy, purity
E-Commerce Data Mining
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Questions?
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