Probabilistic clustering | Morphometric analyzer - NeuroSuites

Probabilistic clustering


Beta version

1. Upload dataset/probabilistic clustering model

Select the dataset and features:

Coming soon...

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Features:

All features selected

Class features (optional):

This algorithm requires a
discrete class feature

Upload full model (clusters + graph):

Formats supported: .zip file
Containing files in the following form:
mean_vector_cluster__i.parquet.gzip, covariance_matrix_cluster__i.parquet.gzip, precision_matrix_cluster__i.csv

To create Apache Parquet gzip files please follow this tutorial:
Apache Parquet files creation.

Clustering model

FGcoaIapjCcYZChdid7gweGRbKpclz8ZtaPlcA29ubdjz8LxjgyictWbqhAiy0xb

Download example Allen model

Download example mtcars model


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Upload pre-model files (clusters):

Formats supported: .zip file
Containing files in the following form:
mean_vector_cluster__i.parquet.gzip, covariance_matrix_cluster__i.parquet.gzip

To create Apache Parquet gzip files please follow this tutorial:
Apache Parquet files creation.

Clustering model

FGcoaIapjCcYZChdid7gweGRbKpclz8ZtaPlcA29ubdjz8LxjgyictWbqhAiy0xb

(Template file)

Use this to upload external info about groups of nodes:

FGcoaIapjCcYZChdid7gweGRbKpclz8ZtaPlcA29ubdjz8LxjgyictWbqhAiy0xb

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Estimated time to finish:

Data uploaded

Additional parameters uploaded

2. Learn the structure of the Probabilistic clustering model

Select the structure learning algorithm:

Learning algorithm:

PC algorithm parameters

Backend:

Grow-Shrink algorithm parameters

Backend:

Incremental Association algorithm parameters

Backend:

Fast Incremental Association algorithm parameters

Backend:

Interleaved Incremental Association algorithm parameters

Backend:

Hill climbing algorithm parameters

Backend:

Hill climbing with tabu search algorithm parameters

Backend:

Chow-Liu tree algorithm parameters

Backend:

Hiton Parents and Children algorithm parameters

Backend:

sparseBn algorithm parameters

Backend:

FGES algorithm parameters

Mode:

Backend:

Max-Min Hill-Climbing algorithm parameters

Backend:

Max-Min Parents and Children algorithm parameters

Backend:

GOBNILP solver algorithm parameters

Coming soon.

Naive Bayes algorithm parameters

Backend:

Tree Augmented Naive Bayes algorithm parameters

Backend:

Multi-dimensional Probabilistic clustering model classifier algorithm parameters

Backend:

Learning structure...

Estimated time to finish:

Structure learned
Check the graph below.

3. Learn the parameters of the Probabilistic clustering model

Select the parameters learning algorithm:

Learning algorithm:

Gaussian maximum likelihood estimation algorithm parameters

Backend:

Discrete maximum likelihood estimation algorithm parameters

Backend:

Discrete Bayesian estimation algorithm parameters

Backend:

Learning parameters...

Estimated time to finish:

Parameters learned
Click on the nodes to check them.

Data

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

Visualize Probabilistic clustering model graph

color_lens Blue nodes are predictor features, red nodes have fixed evidence.

Double click to reset the camera and the colors.

zoom_in

Find one node:

filter_list

Select multiple nodes:

group_work

List of groups:

Category:

compare

Select cluster:

Filter common edges:

or

Sort list by:

equalizer

Nodes by evidences effect:

KL divergence:

Mean:

Standard deviation:

bubble_chart

Groups by evidences effect (KL divergence):

Category:

tune

Graphical lasso parameters:

Alpha

Tol

Max iter

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Evidence:

Loading parameters

nodes ( selected) edges ( selected)

Structures metrics

Export the Probabilistic clustering model

Export Probabilistic clustering model as:

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