In the world of data mining, Microsoft Clustering is a powerful technique that allows us to uncover hidden patterns and groupings within a dataset. Unlike supervised learning techniques, such as Naïve Bayes or Decision Trees, clustering is an unsupervised learning technique. This means that there is no predefined variable to guide the clustering process.

Clustering is particularly useful when dealing with large datasets that have a high number of attributes. Manual grouping becomes impractical in such cases, and we need a specialized technique to identify natural groupings within the data. This is where Microsoft Clustering comes into play.

Let’s explore how we can perform clustering in the Microsoft SQL Server platform. For this example, we will be using the vTargetMail view in the AdventureWorksDW sample database, as we did for previous examples in this series.

To begin, we need to create a data source and a Data Source View. In the wizard, we select AdventureWorksDW as the data source and vTargetMail as the data source view. Next, we choose the relevant attributes for clustering. It is important to select attributes that are likely to contribute to the natural grouping, while excluding those that are less significant.

Once the attributes are selected, we need to define the content types for each attribute. This helps the clustering algorithm understand the nature of the data. For example, numeric attributes like Age and NumberCarsOwned can be classified as discrete, while attributes like YearlyIncome can be classified as continuous.

With the default settings in the data mining wizard, we are now ready to process the data mining structure. This will create a Solution Explorer for the Clustering data mining technique.

After processing the data mining structure, we can view the results through various analysis graphs. The Cluster Diagram provides a visual representation of the cluster distribution. We can select different attributes and their values to explore the clusters in more detail.

The Cluster Profiles view allows us to examine the characteristics of each cluster. We can see how different attributes contribute to the cluster profiles and identify patterns within the clusters.

The Cluster Characteristics view provides additional details about a selected cluster, giving us insights into its unique properties.

Lastly, the Cluster Discrimination view allows us to compare two clusters and understand the differences between them. This can be useful for identifying distinct groups within the dataset.

When working with the Microsoft Clustering algorithm, we can also modify certain parameters to improve the results. The Cluster Count parameter determines the number of clusters to be created. It is important to find the optimal value that balances visualization and model performance.

The Cluster Method parameter allows us to choose between Expectation-Maximization (EM) and K-Means clustering methods. EM uses a probabilistic measure, while K-Means uses Euclidean distance. The choice depends on the nature of the data and the desired clustering outcome.

Other parameters, such as Sample Size and Stopping Tolerance, can be adjusted to optimize the clustering process for large datasets.

Once the clustering model is built, we can use it to predict the cluster for new data. By selecting the Mining Model prediction tab and entering values for each attribute, we can determine which cluster the data belongs to and its probability of belonging to that cluster.

In summary, Microsoft Clustering is a valuable tool in SQL Server for uncovering natural groupings within a dataset. By understanding the clustering process and utilizing the various views and parameters available, we can gain valuable insights and make informed decisions based on the patterns and characteristics discovered.

Stay tuned for more articles in our SQL Server Data Mining Techniques series!