NeuroSuites

Supervised classification | Morphometric analyzer - NeuroSuites

Non probabilistic clustering

1. Features selection

Manual predictor features selection:

No data available. If you want to upload your dataset please go here and then come back here.

Features preprocessing:

Fill missing values (if any):

Remove constant features

Normalize values:

Discretize continuous values

Max number of bins:

This algorithm requires a
discrete class feature

Select the first top features:

Loading dataset...

Estimated time to finish:

Data uploaded

2. Clustering algorithm

Select the learning algorithm:

Selected algorithms:

Agglomerative hierarchical Algorithm hyperparameters

Number of clusters criteria:

max distance:

Linkage:

Distance:

Backend:

K means Algorithm hyperparameters

Backend:

Dbscan Algorithm hyperparameters

epsilon:

min samples:

Distance:

Backend:

Affinity propagation Algorithm hyperparameters

damping:

Backend:

Spectral clustering Algorithm hyperparameters

Number of clusters selection:

Number of clusters:

Number of components:

Backend:

BIRCH Algorithm hyperparameters

Threshold:

Branching factor:

Backend:

OPTICS Algorithm hyperparameters

Min samples:

Distance:

Backend:

Running learning algorithms...

Estimated time to finish:

Learning phase done

3. Clustering result

Non probabilistic clustering

1. Features selection

Manual predictor features selection:

Features preprocessing:

Data uploaded

2. Clustering algorithm

Select the learning algorithm:

Learning phase done

3. Clustering result

Plots: