K means frequently asked questions

K means frequently asked questions

What is K-Means clustering in?
K-Means clustering is an unsupervised machine learning technique available in Sigma Magic that groups similar data points into kkk
 clusters by minimizing intra-cluster variance.
How does Sigma Magic perform K-Means clustering?

Sigma Magic uses the Hartigan-Wong algorithm (by default) with Euclidean distance as the similarity measure. It iteratively assigns data points to clusters and updates centroids until convergence.
What types of data can be used for K-Means clustering?

Sigma Magic supports continuous numerical data for K-Means clustering. Categorical data needs to be converted into numerical form before clustering.
Can Sigma Magic handle missing values in K-Means clustering?
No, missing values must be handled before running the clustering process. You can use mean imputation or remove incomplete data rows.
How do I interpret the cluster plot in Sigma Magic?

The cluster plot visually represents data points and their assigned clusters, showing how distinct or overlapping the clusters are.
Is it possible to visualize high-dimensional data?

Yes, but Sigma Magic reduces dimensionality using PCA (Principal Component Analysis) to project data into a 2D or 3D space for visualization. 
Can K-Means handle outliers effectively in Sigma Magic?
No, K-Means is sensitive to outliers. Use outlier removal techniques before running the algorithm.
Why does my clustering result change every time I run Sigma Magic?

K-Means uses random initialization; different starting centroids can lead to slightly different results. Fixing the random seed can ensure consistency.
What are the limitations of K-Means?

 Requires predefining k, struggles with non-spherical clusters, and is sensitive to outliers.

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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