Tools for

1
Part II

• Tools for
• Knowledge Discovery

2
Knowledge Discovery in Databases

• Chapter 5

3
5.1 A KDD Process Model
4
(No Transcript)
5
(No Transcript)
6
Step 1 Goal Identification

• Define the Problem.
• Choose a Data Mining Tool.
• Estimate Project Cost.
• Estimate Project Completion Time.
• Address Legal Issues.
• Develop a Maintenance Plan.

7
Step 2 Creating a Target Dataset
8
(No Transcript)
9
Step 3 Data Preprocessing

• Noisy Data
• Missing Data

10
Noisy Data

• Locate Duplicate Records.
• Locate Incorrect Attribute Values.
• Smooth Data.

11
Preprocessing Missing Data

• Discard Records With Missing Values.
• Replace Missing Real-valued Items With the
  Class Mean.
• Replace Missing Values With Values Found Within
  Highly Similar Instances.

12
Processing Missing Data While Learning

• Ignore Missing Values.
• Treat Missing Values As Equal Compares.
• Treat Missing values As Unequal Compares.

13
Step 4 Data Transformation

• Data Normalization
• Data Type Conversion
• Attribute and Instance Selection

14
Data Normalization

• Decimal Scaling
• Min-Max Normalization
• Normalization using Z-scores
• Logarithmic Normalization

15
Attribute and Instance Selection

• Eliminating Attributes
• Creating Attributes
• Instance Selection

16
(No Transcript)
17
Step 5 Data Mining

1. Choose training and test data.
2. Designate a set of input attributes.
3. If learning is supervised, choose one or more
   output attributes.
4. Select learning parameter values.
5. Invoke the data mining tool.

18
Step 6 Interpretation and Evaluation

• Statistical analysis.
• Heuristic analysis.
• Experimental analysis.
• Human analysis.

19
Step 7 Taking Action

• Create a report.
• Relocate retail items.
• Mail promotional information.
• Detect fraud.
• Fund new research.

20
5.9 The Crisp-DM Process Model

1. Business understanding
2. Data understanding
3. Data preparation
4. Modeling
5. Evaluation
6. Deployment

21
5.10 Experimenting with ESX
22
A Four-Step Model for Knowledge Discovery

1. Identify the goal.
2. Prepare the data.
3. Apply data mining.
4. Interpret and evaluate the results.

23
Experiment 1 Attribute Evaluation

• Applying the Four-Step Process Model to the
  Credit Screening Dataset

24
(No Transcript)
25
(No Transcript)
26
Experiment 2 Parameter Evaluation

• Applying the Four-Step Process Model to the
  Satellite Image Dataset

27
(No Transcript)