Cluster analysis is a powerful statistical concept that can provide valuable insights into your data when the categories are either vague or unknown. It has applications in various fields such as biology, medicine, business, marketing, social network analysis, and computer science. In this article, we will focus on using k-means clustering in SQL Server to gain a better understanding of cluster analysis.
Let’s consider an example from John Foreman’s book “Data Smart”. Imagine you have a table containing the locations of individuals at a Junior High dance. By performing cluster analysis on this data, you can identify distinct groups within the dataset. For instance, you may discover that there are three clusters: junior high girls, junior high boys, and teachers. These newly discovered categories can lead to asking different questions and gaining better insights into the data.
To demonstrate the concept of cluster analysis in SQL Server, we will use a Junior High School example. The example includes three main components:
- tbl_clusters: Contains the Junior High dance data
- usp_cluster: Performs k-means clustering
- usp_silhouette: Calculates the silhouette
You can download the example scripts and follow the steps provided to set up the Junior High School example in SQL Server. The scripts are compatible with SQL Server versions 2008 and above.
Once you have set up the example, you can perform the cluster analysis by running the “Perform_Clustering_and_Calculate_Silhouette.sql” script. For reporting purposes, ensure that you are using SSRS 2008 or above, or Report Builder 3.0. Open the “clustering_report.rdl” file, connect the data source to the “db_clustering” database, and run the report.
K-means clustering is one of the methods used in cluster analysis. It involves randomly assigning each data point to one of the clusters and finding the cluster centers using the average of each cluster’s data points. The data points are then reassigned to the nearest cluster center, and this process is repeated until the cluster centers stop changing and the best cluster center is found.
To ensure that you have the optimal number of clusters, you can use silhouetting. Silhouetting quantifies how well the data are clustered by providing a “score” for each data point. A score closer to 1 indicates a good match for its cluster, while a lower score suggests a closer match to a neighboring cluster. The average of the scores for all the data points is known as the silhouette. In the Junior High example, we calculated the silhouette for 2 and 3 clusters, and the results confirmed that 3 clusters provided the best analysis.
Calculating the silhouette score for a single data point involves finding the average distance between the data point and all the other points in its cluster, as well as the average distance between the data point and all the other points in neighboring clusters. The silhouette score is then calculated as (Y-X)/C, where X is the average distance within the cluster, Y is the average distance to the nearest neighboring cluster, and C is the greater of the two numbers.
Cluster analysis has the potential to provide valuable insights into your data that may not be available through other means. The example provided in this article is a simplified SQL implementation of cluster analysis, making it accessible to a wide range of users. I hope you found this content useful and can apply it to your own data where the categories are either ambiguous or unknown.
About the author: Stephan Calderon is a college student who has been programming in T-SQL since the age of 12. He has a passion for databases and is currently pursuing a degree in computer science with a focus on database management systems.