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Data mining is a process designed to explore data in search of consistent patterns or systematic relationships between variables. It then validates the findings by applying the detected patterns to new subsets of data. In this blog post, we will discuss how you can extract insights from your data using SQL Server.
What is SQL Server?
SQL Server is a relational database management system (RDMS) developed by Microsoft. It is used to store and retrieve data as required by other software applications. Because of its ability to handle vast amounts of data, it is often used in data mining to analyze patterns and trends.
Simple SQL Commands for Data Mining
Now let’s move onto actual SQL queries that you’ll often use in data mining processes. We will discuss three basics: SELECT, WHERE, and JOIN.
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-- SELECT command is used to select data from a database SELECT * FROM Customers; |
The asterisk (*) is used to select all columns from the “Customers” table.
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-- WHERE command is used to filter the records SELECT * FROM Customers WHERE Country='Germany'; |
This query selects all data from the “Customers” table where the “Country” column equals ‘Germany’.
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-- JOIN command is used to combine rows from two or more tables SELECT Orders.OrderID, Customers.CustomerName FROM Orders INNER JOIN Customers ON Orders.CustomerID = Customers.CustomerID; |
This query selects the “OrderID” and “CustomerName” from the “Orders” and “Customers” tables where the “CustomerID” matches. This is the essence of SQL JOIN command, providing the ability to connect data from multiple tables effectively.
Data Mining Techniques with SQL Server
There are several data mining techniques you can employ using SQL Server, and we will explore the concept of Association Rules.
Association Rules and The Apriori Algorithm
Association rules are widely used to analyze retail basket or transaction data, and are intended to identify strong rules discovered in transaction data using measures of interestingness, based on the concept of strong rules.
An example of Association Rules is ‘Market Basket Analysis’, which is a modeling technique based upon the theory that if you buy a certain group of items, you are more (or less) likely to buy another group of items. In SQL, a simple market basket analysis might look like this:
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-- Market Basket Analysis SELECT P1.ProductName AS Product, P2.ProductName AS OftenBoughtWith FROM SalesOrderDetails AS S1 JOIN Product AS P1 ON S1.ProductID = P1.ProductID JOIN SalesOrderDetails AS S2 ON S1.SalesOrderID = S2.SalesOrderID AND S1.SalesOrderDetailID <> S2.SalesOrderDetailID JOIN Product AS P2 ON S2.ProductID = P2.ProductID GROUP BY P1.ProductName, P2.ProductName; |
This query would return a list with two columns: Product and OftenBoughtWith. These indicate which products are often bought together.
Conclusion
While data mining can be complex, the ability to understand and use SQL commands can simplify the process. Although we’ve only scratched the surface in this post, I hope you’ve learned something valuable for your journey into SQL Server Data Mining.