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(projects:celadons:density)=
# Density-based Clustering

**Task:** Load, rearrange, standardize the celadons data set.

**Solution:**

```{code-cell} ipython3
# your solution
```

+++ {"user_expressions": []}

**Task:** Plot a histogram of pairwise distances to get a feeling for distances in the data set.

**Solution:**

```{code-cell} ipython3
# your solution
```

+++ {"user_expressions": []}

**Task:** Use DBSCAN algorithm for clustering. Print sample names for each cluster (and outliers).

**Solution:**

```{code-cell} ipython3
# your solution
```

+++ {"user_expressions": []}

**Task:** Use OPTICS algorithm for clustering. Choose clusters manually.

**Solution:**

```{code-cell} ipython3
# your solution
```

+++ {"user_expressions": []}

**Task:** Visualize the data set exploiting the tree structure generated by OPTICS algorithm. Label each node with the sample name.

**Solution:**

```{code-cell} ipython3
# your solution
```
