What factors are important for the proper organization of data clustering process?

The following factors are important for the proper organization of data clustering process:

• Choosing the appropriate clustering algorithm: depends on the type of data, task, and requirements (e.g., K-means, hierarchical clustering, DBSCAN).
• Data preprocessing: normalization, removal of outliers and missing values to improve cluster quality.
• Determining the number of clusters: sometimes unknown in advance, so evaluation methods (silhouette, elbow) are used.
• Choosing the distance metric: Euclidean, Manhattan, or others, depending on the nature of the data.
• Assessing cluster quality: using internal and external metrics to ensure that clusters are meaningful.

For example, when clustering users by behavior, it is important to first clean the data, choose an algorithm suitable for uneven groups, and verify how well the resulting clusters reflect real segments.