Clustering in Machine Learning
statistics-clustering_machinelearning/clustering
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flowchart TD 0["ℹ️ Input Dataset\niris"]; style 0 stroke:#2c3143,stroke-width:4px; 1["ℹ️ Input Dataset\ncircles"]; style 1 stroke:#2c3143,stroke-width:4px; 10["Numeric Clustering"]; 2 -->|output| 10; 11["Numeric Clustering"]; 2 -->|output| 11; 12["Numeric Clustering"]; 3 -->|tabular| 12; 13["Numeric Clustering"]; 3 -->|tabular| 13; 14["Numeric Clustering"]; 3 -->|tabular| 14; 15["Scatterplot with ggplot2"]; 5 -->|outfile| 15; 275528af-4af3-4dc6-a958-737f4d2f89bd["Output\nheirarchical_clustering_circles"]; 15 --> 275528af-4af3-4dc6-a958-737f4d2f89bd; style 275528af-4af3-4dc6-a958-737f4d2f89bd stroke:#2c3143,stroke-width:4px; 16["Scatterplot with ggplot2"]; 6 -->|outfile| 16; 870b03ca-6b2c-42ab-bbcc-6c4bbecf8ea9["Output\nkmeans_clustering_circles"]; 16 --> 870b03ca-6b2c-42ab-bbcc-6c4bbecf8ea9; style 870b03ca-6b2c-42ab-bbcc-6c4bbecf8ea9 stroke:#2c3143,stroke-width:4px; 17["Scatterplot with ggplot2"]; 7 -->|outfile| 17; 9664ba05-4664-4ceb-8f93-0974b20bf9e1["Output\ndbscan_clustering_circles"]; 17 --> 9664ba05-4664-4ceb-8f93-0974b20bf9e1; style 9664ba05-4664-4ceb-8f93-0974b20bf9e1 stroke:#2c3143,stroke-width:4px; 18["Scatterplot with ggplot2"]; 9 -->|outfile| 18; d82a4483-7f38-4614-96b4-2a9b3316c808["Output\nheirarchical_clustering_moon"]; 18 --> d82a4483-7f38-4614-96b4-2a9b3316c808; style d82a4483-7f38-4614-96b4-2a9b3316c808 stroke:#2c3143,stroke-width:4px; 19["Scatterplot with ggplot2"]; 10 -->|outfile| 19; 00c0ca58-4ee5-4606-8c1b-c172431e5dbd["Output\ndbscan_clustering_moon"]; 19 --> 00c0ca58-4ee5-4606-8c1b-c172431e5dbd; style 00c0ca58-4ee5-4606-8c1b-c172431e5dbd stroke:#2c3143,stroke-width:4px; 2["ℹ️ Input Dataset\nmoon"]; style 2 stroke:#2c3143,stroke-width:4px; 20["Scatterplot with ggplot2"]; 11 -->|outfile| 20; 5717bf22-83e3-46a2-8f6d-12a1fa41439b["Output\nkmeans_clustering_moon"]; 20 --> 5717bf22-83e3-46a2-8f6d-12a1fa41439b; style 5717bf22-83e3-46a2-8f6d-12a1fa41439b stroke:#2c3143,stroke-width:4px; 21["Scatterplot with ggplot2"]; 12 -->|outfile| 21; 0ace8714-ff3a-4efd-a649-70924b1e6230["Output\nheirarchical_clustering_iris"]; 21 --> 0ace8714-ff3a-4efd-a649-70924b1e6230; style 0ace8714-ff3a-4efd-a649-70924b1e6230 stroke:#2c3143,stroke-width:4px; 22["Scatterplot with ggplot2"]; 13 -->|outfile| 22; e6f69d7d-c9e3-4023-8daf-87f6b0afc3fd["Output\nkmeans_clustering_iris"]; 22 --> e6f69d7d-c9e3-4023-8daf-87f6b0afc3fd; style e6f69d7d-c9e3-4023-8daf-87f6b0afc3fd stroke:#2c3143,stroke-width:4px; 23["Scatterplot with ggplot2"]; 14 -->|outfile| 23; 119d1cde-567a-47f0-99de-00d3557c38a2["Output\ndbscan_clustering_iris"]; 23 --> 119d1cde-567a-47f0-99de-00d3557c38a2; style 119d1cde-567a-47f0-99de-00d3557c38a2 stroke:#2c3143,stroke-width:4px; 3["Convert CSV to tabular"]; 0 -->|output| 3; 4["Scatterplot with ggplot2"]; 1 -->|output| 4; 5["Numeric Clustering"]; 1 -->|output| 5; 6["Numeric Clustering"]; 1 -->|output| 6; 7["Numeric Clustering"]; 1 -->|output| 7; 8["Scatterplot with ggplot2"]; 2 -->|output| 8; 9["Numeric Clustering"]; 2 -->|output| 9;
Inputs
Input | Label |
---|---|
Input dataset | iris |
Input dataset | circles |
Input dataset | moon |
Outputs
From | Output | Label |
---|---|---|
toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/2.2.1+galaxy2 | Scatterplot with ggplot2 | |
toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/2.2.1+galaxy2 | Scatterplot with ggplot2 | |
toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/2.2.1+galaxy2 | Scatterplot with ggplot2 | |
toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/2.2.1+galaxy2 | Scatterplot with ggplot2 | |
toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/2.2.1+galaxy2 | Scatterplot with ggplot2 | |
toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/2.2.1+galaxy2 | Scatterplot with ggplot2 | |
toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/2.2.1+galaxy2 | Scatterplot with ggplot2 | |
toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/2.2.1+galaxy2 | Scatterplot with ggplot2 | |
toolshed.g2.bx.psu.edu/repos/iuc/ggplot2_point/ggplot2_point/2.2.1+galaxy2 | Scatterplot with ggplot2 |
Tools
To use these workflows in Galaxy you can either click the links to download the workflows, or you can right-click and copy the link to the workflow which can be used in the Galaxy form to import workflows.
Importing into Galaxy
Below are the instructions for importing these workflows directly into your Galaxy server of choice to start using them!Hands-on: Importing a workflow
- Click on Workflow on the top menu bar of Galaxy. You will see a list of all your workflows.
- Click on galaxy-upload Import at the top-right of the screen
- Provide your workflow
- Option 1: Paste the URL of the workflow into the box labelled “Archived Workflow URL”
- Option 2: Upload the workflow file in the box labelled “Archived Workflow File”
- Click the Import workflow button
Below is a short video demonstrating how to import a workflow from GitHub using this procedure:
Video: Importing a workflow from URL
Version History
Version | Commit | Time | Comments |
---|---|---|---|
2 | 6e1f4b5d3 | 2020-07-23 07:11:05 | test for clustering workflow and update to the workflow to have workflow_outputs |
1 | 3741978d0 | 2020-05-08 12:10:38 | images are modified, workflow is added, zenodo is updatet and tiny changes |
For Admins
Installing the workflow tools
wget https://training.galaxyproject.org/training-material/topics/statistics/tutorials/clustering_machinelearning/workflows/clustering.ga -O workflow.ga workflow-to-tools -w workflow.ga -o tools.yaml shed-tools install -g GALAXY -a API_KEY -t tools.yaml workflow-install -g GALAXY -a API_KEY -w workflow.ga --publish-workflows