Download Data Mining Example

Data Mining Process
1. The SEMMA method from SAS institute
Sample the data by creating one of more tables
Explore the data by searching for anticipated relationships, unanticipated trends
and anomalies in order to gain understanding and ideas.
Modify the data by creating, selecting, and transforming the variables.
Model the data by using the analytical tools to search for a combination of the
data that reliably predicts a desired outcome.
Assess the data by evaluating the usefulness and reliability of the findings from
the data mining process.
Input
data
Plot
data
Transform
variables
Select
variables/
features
Partition
data
Regression
Decision tree
Assessment
Cost
/score
Neural net
Fig. 1- SAS Institute Enterprise Miner Analysis Diagram (uses SEMMA)
2. Realistic data mining process
Caveat:
 the process as well as all phases/steps are iterative;
 the sequence of steps/phases depends on the real-life circumstances.
Data preparation phase (i.e. “turn a mess of data into an organized whole”):
Data cleaning: consistency, stale information, typos
Missing values: fill them in, ignore them
Data derivation: derive composite features
Merging data from several databases: using a flat file (usually)
Transforming raw data: normalizing, smoothing, scaling, encoding
Dealing with outliers
Define a study (i.e. selecting data to mine and output, phase I; i.e. “what are we
doing here”):
Define the goal, for example:
 Define the characteristics (i.e. the profile) of patients who have allergies
 Profile the patients who recover in 0-2 weeks, 2-3 weeks, 3-6weeks, and 6+
weeks
 Profile patients who use mild or high pain relievers in order to reduce pain
Identify which features are of interest, for example:
 What features are useful to profiling people with allergies?
 How descriptive are the fields in the current features?
 What types of features should we include?
Identify input and output features
Selecting data to mine and output, phase II (i.e. “how can I mine only a subset of
data and get good results if I have a large database”):
“Shrink” the table along X axis (i.e. reduce number of columns either by deleting or
merging features):
 Feature selection and reduction (e.g. by comparing mean and variance, by
entropy, by principal component analysis)
 Feature composition (e.g. by merging using principal components)
 Reducing feature values (e.g. by discretizing feature values using binning)
 Merging input intervals (e.g. by Chi-square)
“Shrink” the table along Y axis (i.e. reduce number of rows either by deleting or merging
samples/cases):
 Cases selection and reduction
Build the model and mine it:
Pick suitable data mining strategy(ies) and tool(s)
Validate the model:
Test the model on data which wasn’t used to build the model.
If you built several models (you did, most likely), determine which ones are the best
 Calculate the error

Issues: is the model accurate, understandable, lets you know where it’s confident
and where it isn’t and why (i.e. provides quantitative assessment with complex
conclusions), lets you trace which inputs affect the output; is it fast
3. Data Mining Techniques Anatomy
Each data mining technique can be classified into the following categories based on the
functionality provided by the technique (i.e. the primary data mining task):
 Classification
 Regression
 Clustering
 Summarization
 Dependency modeling
 Change and Deviation detection
All data mining techniques can also be classified as using:
 supervised or unsupervised learning; and using
 inductive or deductive learning.
4. Menu of data mining strategies/tools
Statistical methods:
Bayesian inference
Logistic regression
ANOVA analysis
Log-linear models
Cluster analysis:
Divisible algorithms
Agglomerative hierarchical clustering
Partitional clustering
Incremental clustering
Decision trees and rules:
CLS algorithm
1D3 algorithm
C4.5 algorithm
Prunning algorithm
Association rules:
Market basket analysis
Apriori algorithm
www path traversal patterns
Text mining
Artificial neural nets:
Multilayer perceptrons with backpropagation learning
Kohonen networks
Genetic algorithms
Fuzzy Inference Systems
N-dimensional visualization methods