By: Dallas Snider | Updated: 2015-01-21 | Comments | Related: > TSQL
Problem
In data mining, we sometimes need to perform techniques such as Z-score normalization on numeric data type columns to prevent one column from skewing or dominating the models produced by the machine learning algorithms. How can we perform this in SQL Server with T-SQL code?
Solution
In this tip, we will demonstrate how to use T-SQL to perform a Z-score normalization. The Z-score normalized value equals (the original column value minus the mean column value) divided by the standard deviation of the column values. We will use T-SQL's AVG() function to calculate the mean and the STDEV() function to calculate the standard deviation.
We will create a table that contains two data columns, AttributeX and AttributeY, and perform the Z-score normalization on each of these columns.
IF EXISTS (SELECT name FROM sys.tables WHERE name = N'tblExample') DROP TABLE dbo.tblExample go create table dbo.tblExample ( pKey integer identity(1,1) PRIMARY KEY, AttributeX decimal(7,3), AttributeY decimal(7,3) )
Next, we will populate this table with 27 rows of data. AttributeY will be 10 times the value of AttributeX.
insert into dbo.tblExample values (24, 240) insert into dbo.tblExample values (26, 260) insert into dbo.tblExample values (27, 270) insert into dbo.tblExample values (27, 270) insert into dbo.tblExample values (30, 300) insert into dbo.tblExample values (31, 310) insert into dbo.tblExample values (31, 310) insert into dbo.tblExample values (32, 320) insert into dbo.tblExample values (33, 330) insert into dbo.tblExample values (33, 330) insert into dbo.tblExample values (36, 360) insert into dbo.tblExample values (36, 360) insert into dbo.tblExample values (36, 360) insert into dbo.tblExample values (36, 360) insert into dbo.tblExample values (41, 410) insert into dbo.tblExample values (44, 440) insert into dbo.tblExample values (44, 440) insert into dbo.tblExample values (46, 460) insert into dbo.tblExample values (46, 460) insert into dbo.tblExample values (46, 460) insert into dbo.tblExample values (46, 460) insert into dbo.tblExample values (47, 470) insert into dbo.tblExample values (51, 510) insert into dbo.tblExample values (56, 560) insert into dbo.tblExample values (57, 570) insert into dbo.tblExample values (63, 630) insert into dbo.tblExample values (81, 810)
After inserting the rows, we will select the row count and the mean and standard deviation values from our data columns.
select count(*) as RowQuantity, avg(AttributeX) as MeanX, stdev(AttributeX) as StandardDeviationX, avg(AttributeY) as MeanY, stdev(AttributeY) as StandardDeviationY from dbo.tblExample
Now we will use the T-SQL code below to perform Z-score normalizations on AttributeX and AttributeY.
--Declare the variables declare @meanX as decimal(7,3) declare @standardDeviationX as decimal(7,3) declare @meanY as decimal(7,3) declare @standardDeviationY as decimal(7,3) --Set the variables set @meanX=(select avg(AttributeX) from dbo.tblExample) set @standardDeviationX=(select stdev(AttributeX) from dbo.tblExample) set @meanY=(select avg(AttributeY) from dbo.tblExample) set @standardDeviationY=(select stdev(AttributeY) from dbo.tblExample) --Perform the normalization select AttributeX, (AttributeX - @meanX)/(@standardDeviationX) as NormalizedX, AttributeY, (AttributeY - @meanY)/(@standardDeviationY) as NormalizedY from dbo.tblExample
The results from the previous query are shown below. Notice how the normalized values for AttributeX and AttributeY are equal on each row, while the original values differ. Also notice how all normalized values less than the mean are negative, while those values greater than the mean are positive.
Next Steps
Normalized values can be inserted into a table and then read into data mining algorithms. You can read more about SQL Server data mining in the following tips:
- SQL Server 2012 Analysis Services Association Rules Data Mining Example
- Explaining the Calculations of Probability and Importance for Complex Association Rules in SQL Server 2012 Analysis Services
- Classic Machine Learning Example In SQL Server Analysis Services
- Microsoft Naïve Bayes Data Mining Model in SQL Server Analysis Services
- Data Mining Clustering Example in SQL Server Analysis Services SSAS
- SQL Server Analysis Services Glossary
About the author
Dr. Dallas Snider is an Assistant Professor in the Computer Science Department at the University of West Florida and has 18+ years of SQL experience.This author pledges the content of this article is based on professional experience and not AI generated.
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Article Last Updated: 2015-01-21
