Choosing the right way to group your data can be tricky. Here are a few reasons why:
Data Type: It can be hard to understand how your data is spread out or its shape.
Size Problems: Some methods don’t work well when the data set is really big, which can slow things down.
Settings Matter: Different methods need specific adjustments, and this can take a lot of time.
To help with these challenges, you can try using some methods to test and check how good your clustering is. For example, you can look at silhouette scores or use the elbow method.
Choosing the right way to group your data can be tricky. Here are a few reasons why:
Data Type: It can be hard to understand how your data is spread out or its shape.
Size Problems: Some methods don’t work well when the data set is really big, which can slow things down.
Settings Matter: Different methods need specific adjustments, and this can take a lot of time.
To help with these challenges, you can try using some methods to test and check how good your clustering is. For example, you can look at silhouette scores or use the elbow method.