What are the main challenges of clustering in data analysis?

What are the main challenges of clustering in data analysis?

Clustering can be challenging due to issues like determining the right number of clusters, handling noise, and high dimensionality.

How do you decide the number of clusters to use?

You can use methods like the Elbow Method, Silhouette Score, or Gap Statistic to help determine the optimal number of clusters.

What about dealing with noise in the data?

Dealing with noise often involves preprocessing steps like outlier detection and removal, or using robust clustering algorithms.

Can clustering work well with high-dimensional data?

High-dimensional data can be problematic due to the curse of dimensionality, but techniques like PCA can help reduce dimensions before clustering.

What are some common clustering algorithms?

Common clustering algorithms include K-Means, Hierarchical Clustering, DBSCAN, and Gaussian Mixture Models.