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Data Mining for Business Analytics (Taxonomy (Prediction (Classification,…
Data Mining for Business Analytics
Data
Categorical
Norminal
mutual exclusive / not order
ex. male/female, single/married/divorce
Ordinal
rank among data
ex. credit score: low/medium/high
Numerical
Interval
order and difference is meaningful
Ratio
zero means none
temperature in F and C
is not
ratio :warning:
0
doesn't mean
no temperature
temperature in K
is
ratio
0
means
no temperature
Taxonomy
Prediction
Classification
Regression
Association
Link analysis
Sequence analysis
Clustering
Outlier analysis
Data Mining Process
standard processes
CRIPS-DM
Business understanding
Data understanding :recycle:
#
Data preparation :!:
Data consolidation
Data cleaning
Data transformation
Data reduction
Model building :recycle:
#
Testing and evaluation :recycle:
#
Deployment
SEMMA
KDD
Classification Techniques
Decision trees
divide-and-conquer method
DT algorithms
splitting criteria
Gini index :star:
Information gain
Chi-square statistics :star:
stopping criteria
pruning
Cluster analysis
automatic identification of
natural groups
of things
analysis methods
k-means algorithms :star:
neural networks
fuzzy logic
genetic algorithm
Association Rule Mining :star:
Find
interesting
relationship b/w variables
parameters
support
confidence
lift