1. Data Mining: Concepts and Techniques — Slides for Textbook — — Appendix A —
©Jiawei Han and Micheline Kamber
Slides contributed by Jian Pei
Intelligent Database Systems Research Lab
School of Computing Science
Simon Fraser University, Canada
November 21, 2018
Data Mining: Concepts and Techniques

2. Appendix A: An Introduction to Microsoft’s OLE OLDB for Data Mining
Overview and design philosophy
Basic components
Data set components
Data mining models
Operations on data model
Concluding remarks
November 21, 2018
Data Mining: Concepts and Techniques

3. Why OLE DB for Data Mining?
Industry standard is critical for data mining development, usage, interoperability, and exchange
OLEDB for DM is a natural evolution from OLEDB and OLDB for OLAP
Building mining applications over relational databases is nontrivial
Need different customized data mining algorithms and methods
Significant work on the part of application builders
Goal: ease the burden of developing mining applications in large relational databases
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Data Mining: Concepts and Techniques

4. Motivation of OLE DB for DM
Facilitate deployment of data mining models
Generating data mining models
Store, maintain and refresh models as data is updated
Programmatically use the model on other data set
Browse models
Enable enterprise application developers to participate in building data mining solutions
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Data Mining: Concepts and Techniques

5. Features of OLE DB for DM
Independent of provider or software
Not specialized to any specific mining model
Structured to cater to all well-known mining models
Part of upcoming release of Microsoft SQL Server 2000
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Data Mining: Concepts and Techniques

6. Data Mining: Concepts and Techniques
Overview
Data mining
applications
Core relational engine exposes OLE DB in a language-based API
Analysis server exposes OLE DB OLAP and OLE DB DM
Maintain SQL metaphor
Reuse existing notions
OLE DB OLAP/DM
Analysis Server
OLE DB
RDB engine
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Data Mining: Concepts and Techniques

7. Key Operations to Support Data Mining Models
Define a mining model
Attributes to be predicted
Attributes to be used for prediction
Algorithm used to build the model
Populate a mining model from training data
Predict attributes for new data
Browse a mining model fro reporting and visualization
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Data Mining: Concepts and Techniques

8. DMM As Analogous to A Table in SQL
Create a data mining module object
CREATE MINING MODEL [model_name]
Insert training data into the model and train it
INSERT INTO [model_name]
Use the data mining model
SELECT relation_name.[id], [model_name].[predict_attr]
consult DMM content in order to make predictions and browse statistics obtained by the model
Using DELETE to empty/reset
Predictions on datasets: prediction join between a model and a data set (tables)
Deploy DMM by just writing SQL queries!
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Data Mining: Concepts and Techniques

9. Data Mining: Concepts and Techniques
Two Basic Components
Cases/caseset: input data
A table or nested tables (for hierarchical data)
Data mining model (DMM): a special type of table
A caseset is associated with a DMM and meta-info while creating a DMM
Save mining algorithm and resulting abstraction instead of data itself
Fundamental operations: CREATE, INSERT INTO, PREDICTION JOIN, SELECT, DELETE FROM, and DROP
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Data Mining: Concepts and Techniques

10. Flatterned Representation of Caseset
Customers
Customer ID
Gender
Hair Color
Age
Age Prob
Product Purchases
Customer ID
Product Name
Quantity
Product Type
Problem: Lots of replication!
CID
Gend
Hair
Age
Age prob
Prod
Quan
Type
Car
Car prob 1
Male
Black 35
100% TV
Elec
VCR
Ham 6
Food
Van
50%
Car Owernership
Customer ID
Car
Car Prob
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Data Mining: Concepts and Techniques

11. Logical Nested Table Representation of Caseset
Use Data Shaping Service to generate a hierarchical rowset
Part of Microsoft Data Access Components (MDAC) products
CID
Gend
Hair
Age
Age prob
Product Purchases
Car Ownership
Prod
Quan
Type
Car
Car prob 1
Male
Black 35
100% TV
Elec
VCR
Van
50%
Ham 6
Food
November 21, 2018
Data Mining: Concepts and Techniques

12. More About Nested Table
Not necessary for the storage subsystem to support nested records
Cases are only instantiated as nested rowsets prior to training/predicting data mining models
Same physical data may be used to generate different casesets
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Data Mining: Concepts and Techniques

13. Defining A Data Mining Model
The name of the model
The algorithm and parameters
The columns of caseset and the relationships among columns
“Source columns” and “prediction columns”
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Data Mining: Concepts and Techniques

14. Data Mining: Concepts and Techniques
Example
CREATE MINING MODEL [Age Prediction] %Name of Model (
[Customer ID] LONG KEY, %source column
[Gender] TEXT DISCRETE, %source column
[Age] Double DISCRETIZED() PREDICT, %prediction column
[Product Purchases] TABLE %source column
[Product Name] TEXT KEY, %source column
[Quantity] DOUBLE NORMAL CONTINUOUS, %source column
[Product Type] TEXT DISCRETE RELATED TO [Product Name]
%source column ))
USING [Decision_Trees_101] %Mining algorithm used
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Data Mining: Concepts and Techniques

15. Data Mining: Concepts and Techniques
Column Specifiers
KEY
ATTRIBUTE
RELATION (RELATED TO clause)
QUALIFIER (OF clause)
PROBABILITY: [0, 1]
VARIANCE
SUPPORT
PROBABILITY-VARIANCE
ORDER
TABLE
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Data Mining: Concepts and Techniques

16. Data Mining: Concepts and Techniques
Attribute Types
DISCRETE
ORDERED
CYCLICAL
CONTINOUS
DISCRETIZED
SEQUENCE_TIME
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Data Mining: Concepts and Techniques

17. Data Mining: Concepts and Techniques
Populating A DMM
Use INSERT INTO statement
Consuming a case using the data mining model
Use SHAPE statement to create the nested table from the input data
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Data Mining: Concepts and Techniques

18. Example: Populating a DMM
INSERT INTO [Age Prediction] (
[Customer ID], [Gender], [Age],
[Product Purchases](SKIP, [Product Name], [Quantity], [Product Type]) )
SHAPE
{SELECT [Customer ID], [Gender], [Age] FROM Customers ORDER BY [Customer ID]}
APPEND
{SELECT [CustID], {product Name], [Quantity], [Product Type] FROM Sales
ORDER BY [CustID]}
RELATE [Customer ID] TO [CustID]
AS [Product Purchases]
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Data Mining: Concepts and Techniques

19. Using Data Model to Predict
Prediction join
Prediction on dataset D using DMM M
Different to equi-join
DMM: a “truth table”
SELECT statement associated with PREDICTION JOIN specifies values extracted from DMM
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Data Mining: Concepts and Techniques

20. Example: Using a DMM in Prediction
SELECT t.[Customer ID], [Age Prediction].[Age]
FROM [Age Prediction]
PRECTION JOIN
(SHAPE
{SELECT [Customer ID], [Gender] FROM Customers ORDER BY [Customer ID]}
APPEND (
{SELECT [CustID], [Product Name], [Quantity] FROM Sales ORDER BY [CustID]}
RELATE [Customer ID] TO [CustID] )
AS [Product Purchases]
AS t
ON [Age Prediction].[Gender]=t.[Gender] AND
[Age Prediction].[Product Purchases].[Product Name]=t.[Product Purchases].[Product Name] AND
[Age Prediction].[Product Purchases].[Quantity]=t.[Product Purchases].[Quantity]
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Data Mining: Concepts and Techniques

21. Data Mining: Concepts and Techniques
Browsing DMM
What is in a DMM?
Rules, formulas, trees, …, etc
Browsing DMM
Visualization
November 21, 2018
Data Mining: Concepts and Techniques

22. Data Mining: Concepts and Techniques
Concluding Remarks
OLE DB for DM integrates data mining and database systems
A good standard for mining application builders
How can we be involved?
Provide association/sequential pattern mining modules for OLE DB for DM?
Design more concrete language primitives?
References
November 21, 2018
Data Mining: Concepts and Techniques

23. Data Mining: Concepts and Techniques
Thank you !!!
November 21, 2018
Data Mining: Concepts and Techniques