Hierarchical clusters frequently asked questions

Hierarchical clusters frequently asked questions

What is Hierarchical Clustering?
Hierarchical clustering in Sigma Magic is a data analysis technique used to group similar data points into a hierarchy of clusters. It builds a tree-like structure called a dendrogram to visualize relationships between clusters.
What are the types of Hierarchical Clustering available in Sigma Magic?

Sigma Magic supports both Agglomerative (Bottom-Up) and Divisive (Top-Down) clustering. Agglomerative is more commonly used.
What linkage methods are available in Sigma Magic?
  • Single Linkage: Closest points between clusters
  • Complete Linkage: Farthest points between clusters
  • Average Linkage: Mean distance between all points in clusters
  • Centroid Linkage: Distance between cluster centroids
  • Ward’s Method: Minimizes variance within clusters
    •
    Do I need to specify the number of clusters in advance?
    No, hierarchical clustering does not require predefining the number of clusters. You can determine an optimal number by analyzing the dendrogram.
    What type of data can be used for hierarchical clustering in Sigma Magic?
    Hierarchical clustering is used for continuous data. If categorical variables exist, they must be converted using techniques like one-hot encoding.
    What is a dendrogram, and why is it useful?
    A dendrogram is a tree-like diagram that shows how clusters are formed. It helps in deciding the number of clusters by visually inspecting the height at which branches merge.
    What are the real-world applications of hierarchical clustering?
    • Market Segmentation
    • Customer Behavior Analysis
    • Document Classification
    • Genetic Data Clustering
    • Image Segmentation
    How does hierarchical clustering handle missing data?

    Missing values should be handled before running clustering, either by removing rows with missing data or imputing missing values.
    Is it necessary to standardize the data before clustering?

    Standardization ensures that all variables contribute equally. Sigma Magic provides an option to standardize automatically, but in the image, it was set to False (not applied).
    ​
     Reference: Some of the text in this article has been generated using AI tools such as ChatGPT and edited for content and accuracy.

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