Implementing Data Mining Solutions

Introduction

Data mining in SQL Server Analysis Services (SSAS) enables you to discover patterns, predict outcomes, and integrate predictive insights directly into your business intelligence solutions. This guide walks you through the end‑to‑end process of building, training, validating, and deploying a data mining model.

Prerequisites

SQL Server 2019 (or later) with Analysis Services installed.
SQL Server Data Tools (SSDT) or Azure Data Studio with the Analysis Services extension.
Access to a relational data source (e.g., AdventureWorksDW).
Basic knowledge of OLAP cubes and tabular models.

Implementation Steps

1. Create a Data Mining Project

In SSDT, select File → New → Project, choose Analysis Services, and then Data Mining Project. Give the project a meaningful name.

2. Define the Mining Structure

Right‑click the project, choose New Mining Structure.
Select a data source view (DSV) that contains your training data.
Choose a mining algorithm (e.g., Microsoft_Clustering).
Map the Case ID, Input Columns, and Prediction Column.

3. Train the Model

Deploy the mining structure to the SSAS instance. The deployment process automatically creates the model and runs the training algorithm.

4. Validate the Model

Use the Prediction Accuracy tab in the mining model designer to evaluate performance metrics such as:

Accuracy
Precision / Recall
Confusion Matrix

5. Deploy the Model to Production

After validation, redeploy the mining model to the production server. You can now query the model using MDX or DMX.

Sample DMX Queries

SELECT
    *
FROM
    [ClusteringStructure].[ClusteringModel]
WHERE
    NATIVE PREDICTION (
        [Gender], [Age], [Income],
        [Occupation] AS PredictedOccupation
    )
    USING [MyClusteringModel];

SELECT
    [Customer].[Customer].[Customer].Members ON ROWS,
    Predict([ClusteringModel].[Cluster]) ON COLUMNS
FROM
    [ClusteringModel];

Best Practices

Normalize numeric data before training.
Use feature selection to reduce dimensionality.
Re‑train models periodically with fresh data.
Monitor model drift using the Model Management tools.

Data Mining