diff --git a/topics/statistics/tutorials/clustering_machinelearning/data-library.yaml b/topics/statistics/tutorials/clustering_machinelearning/data-library.yaml index 02fd6ee52ce566..b87a52c455614c 100644 --- a/topics/statistics/tutorials/clustering_machinelearning/data-library.yaml +++ b/topics/statistics/tutorials/clustering_machinelearning/data-library.yaml @@ -6,15 +6,22 @@ destination: synopsis: Galaxy Training Network Material. See https://training.galaxyproject.org items: - name: Statistics and machine learning - description: Using clustering algorithms as an unsupervised machine learning methods using Galaxy - tools + description: Using clustering algorithms as an unsupervised machine learning methods using Galaxy tools items: - name: Clustering in Machine Learning items: - - name: 'DOI: 10.5281/zenodo.1319069' + - name: 'DOI: 10.5281/zenodo.3813447' description: latest items: - - url: https://zenodo.org/api/files/e6f59ef2-4d13-4033-8727-750665c38af9/iris.csv + - url: https://zenodo.org/record/3813447/files/iris.csv src: url ext: csv - info: https://zenodo.org/record/1319069 \ No newline at end of file + info: https://zenodo.org/record/3813447#.XrVJJHUzaV4 + - url: https://zenodo.org/record/3813447/files/moon.csv + src: url + ext: csv + info: https://zenodo.org/record/3813447#.XrVJJHUzaV4 + - url: https://zenodo.org/record/3813447/files/circles.csv + src: url + ext: csv + info: https://zenodo.org/record/3813447#.XrVJJHUzaV4 \ No newline at end of file diff --git a/topics/statistics/tutorials/clustering_machinelearning/images/circles_clustering.png b/topics/statistics/tutorials/clustering_machinelearning/images/circles_clustering.png index 8ed14d2cd4cda9..ce37687b796346 100644 Binary files a/topics/statistics/tutorials/clustering_machinelearning/images/circles_clustering.png and b/topics/statistics/tutorials/clustering_machinelearning/images/circles_clustering.png differ diff --git a/topics/statistics/tutorials/clustering_machinelearning/images/data_after_clustering_old.png b/topics/statistics/tutorials/clustering_machinelearning/images/data_after_clustering_old.png deleted file mode 100644 index 7673f52560047a..00000000000000 Binary files a/topics/statistics/tutorials/clustering_machinelearning/images/data_after_clustering_old.png and /dev/null differ diff --git a/topics/statistics/tutorials/clustering_machinelearning/images/data_before_clustering_old.png 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a/topics/statistics/tutorials/clustering_machinelearning/images/moon_clustering.png b/topics/statistics/tutorials/clustering_machinelearning/images/moon_clustering.png index 7a72e239aa5ea9..62bf77ba695a8e 100644 Binary files a/topics/statistics/tutorials/clustering_machinelearning/images/moon_clustering.png and b/topics/statistics/tutorials/clustering_machinelearning/images/moon_clustering.png differ diff --git a/topics/statistics/tutorials/clustering_machinelearning/tutorial.md b/topics/statistics/tutorials/clustering_machinelearning/tutorial.md index dedb78c02ebceb..39f730d7b32d0b 100644 --- a/topics/statistics/tutorials/clustering_machinelearning/tutorial.md +++ b/topics/statistics/tutorials/clustering_machinelearning/tutorial.md @@ -2,7 +2,7 @@ layout: tutorial_hands_on title: 'Clustering in Machine Learning' -zenodo_link: https://zenodo.org/... +zenodo_link: https://zenodo.org/record/3813447 questions: - How to use clustering algorithms to categorized data in different clusters objectives: @@ -151,10 +151,10 @@ Now we will apply hierarchical clustering to Iris data set, and learn to measure > ### {% icon hands_on %} Hands-on: Data upload > -> 1. **Import** {% icon galaxy-upload %} the file `iris.csv` from [Zenodo](https://zenodo.org/record/1319069/files/iris.csv) or from the data library +> 1. **Import** {% icon galaxy-upload %} the file `iris.csv` from [Zenodo](https://zenodo.org/record/3813447/files/iris.csv) or from the data library > > ``` -> https://zenodo.org/record/1319069/files/iris.csv +> https://zenodo.org/record/3813447/files/iris.csv > ``` > > {% include snippets/import_via_link.md %} @@ -382,7 +382,7 @@ DBSCAN does not require one to specify the number of clusters in the data a prio > > 2. **View** {% icon galaxy-eye%} the resulting plot: > -> 3. Rename to `k-means scatter plot` +> 3. Rename to `DBSCAN scatter plot` > ![data](images/dbscan_scatter.png "DBSCAN Clustering Scatter Plot") @@ -406,7 +406,7 @@ the algorithm using intrinsic measures. # Appying the clustering algorithms on the other datasets -You can do the same steps on the other data sets, moon and circles. First, import the data files from [Zenodo]({{ page.zenodo_link }}) or a data library: `moon tabular` and `circles tabular` +You can do the same steps on the other data sets, moon and circles. First, import the data files, [moon.csv](https://zenodo.org/record/3813447/files/moon.csv) and [circles.csv](https://zenodo.org/record/3813447/files/circles.csv) from Zenodo or data library and rename them to `moon` and `circles` respectively. > ### {% icon hands_on %} Hands-on: Visualize scatter plot of data > @@ -463,7 +463,7 @@ Then , you can visulaze the clustering result using the following steps: > 2. **View** {% icon galaxy-eye%} the resulting plot: -In the next steps, you can apply these three algorithms (hierarchical, k-means and DBSCAN) in simlar steps to moon and circles datasets. For the DBSCAN algorithm, the parameters are not the default parameters and you should set them as follows: for the circles data set (Maximum neighborhood distance=0.2 and Minimal core point density=5) and for the moon datasets (Maximum neighborhood distance=0.3 and Minimal core point density=4). You can see the scatter plots of the clustering results in Figure 13 and Figure 14. +In the next steps, you can apply these three algorithms (hierarchical, k-means and DBSCAN) in simlar steps to moon and circles datasets. In k-means algorithm, k=2 and for the DBSCAN algorithm, the parameters are not the default parameters and you should set them as follows: for the circles data set (Maximum neighborhood distance=0.2 and Minimal core point density=5) and for the moon datasets (Maximum neighborhood distance=0.3 and Minimal core point density=4). You can see the scatter plots of the clustering results in Figure 13 and Figure 14. > ![data](images/circles_clustering.png "Plot of Clustering Algorithms on Circles Data") diff --git a/topics/statistics/tutorials/clustering_machinelearning/workflows/clustering.ga b/topics/statistics/tutorials/clustering_machinelearning/workflows/clustering.ga new file mode 100644 index 00000000000000..a05745d123c474 --- /dev/null +++ b/topics/statistics/tutorials/clustering_machinelearning/workflows/clustering.ga @@ -0,0 +1,885 @@ +{ + "a_galaxy_workflow": "true", + "annotation": "Clustering in Machine Learning", + "tags":[ + "statistics", + "clustering", + "ml" + ], + "format-version": "0.1", + "name": "Clustering in Machine Learning", + "steps": { + "0": { + "annotation": "", + "content_id": null, + "errors": null, + "id": 0, + "input_connections": {}, + "inputs": [ + { + "description": "", + "name": "iris" + } + ], + "label": "iris", + "name": "Input dataset", + "outputs": [], + "position": { + 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