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Data Warehousing and Mining, Silberschatz, A., Korth, H., & Sudarshan,…
Data Warehousing and Mining
Data Warehousing
gather data
destination-driven architecture
source-driven architecture
Data Mining
pick useful types of patterns
prediction
associations
descriptive patterns
Classification
training instances
partition the given data into
disjoint groups
Decision-Tree Classifiers
start at the root and traverse the tree to reach
a leaf
Building Decision-Tree Classifiers
greedy algorithm
partitioning attribute
partitioning conditions
Best Splits
maximum purity
measure of purity
Gini measure
entropy measure
information gain
information gain ratio
Finding Best Splits
Attributes
continuous valued
categorical
binary splits
multiway splits
complicated
Decision-Tree Construction Algorithm
evaluate different attributes and
different partitioning conditions
classification rules from a decision tree
Other Types of Classifiers
neural-net classifiers
, Bayesian classifiers
Regression
inear regression
curve fitting.
Validating a Classifier
Recall
sensitivity
Precision
Accuracy
Specificity
Association Rules
Support
Confidence
Other Types of Associations
deviation
correlations
Silberschatz, A., Korth, H., & Sudarshan, S. (2019). Database System Concepts (7a ed.). McGraw-Hill.
Alvaro Moreira Villalobos