Clustering

This dataset has 12 columns and 1225 rows.
The preview is meant for you to glance at the data and understand it.
This view shows all columns and the first 100 rows.

Choose the algorithm you want to use for clustering. If you're unsure, try them one by one.

K-Means

Group points closest to each other on features

Mini Batch K-Means

Same as K-Means, but faster and slightly worse

Spectral Clustering

Same as K-Means, but reduces number of features first

Affinity Propagation

Find most representative entities and group nearby ones

Mean-Shift

Find smooth blobs

Ward

Build clusters one-by-one using hierarchically

Density Based Scan

Find dense regions (of any shape) seperated by sparse regions

HDBSCAN

Same as HBSCAN, but density of regions can vary

To override the default number of clusters, pick a value. (Leave it blank for the optimal default.)

Number of clusters:

For help or information regarding the application, please email contact@gramener.com.

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