[DOC] Add example notebook for using aeon distances with sklearn clusterers

docs/examples.md

@@ -165,6 +165,17 @@ Partitional TSCL
 
 :::
 
+:::{grid-item-card}
+:img-top: examples/clustering/img/sklearn_clustering.png
+:class-img-top: aeon-card-image-m
+:link: /examples/clustering/sklearn_clustering_with_aeon_distances.ipynb
+:link-type: ref
+:text-align: center
+
+Using aeon Distances with sklearn Clusterers
+
+:::
+
 ::::
 
 ## Transformation

examples/clustering/clustering.ipynb

@@ -2,6 +2,9 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "# Time Series Clustering\n",
     "\n",
@@ -23,13 +26,13 @@
     "erative [16], Feature K-means [17], Feature K-medoids [17], U-shapelets [18],\n",
     "USSL [19], RSFS [20], NDFS [21], Deep learning and dimensionality reduction\n",
     "approaches see [22]"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "## Clustering notebooks\n",
     "\n",
@@ -41,6 +44,8 @@
     "these can be used in conjunction with `aeon` elastic distances. See the [sklearn and\n",
     "aeon distances](../distances/sklearn_distances.ipynb) notebook.\n",
     "\n",
+    "- For more detailed examples of using `aeon` distances with sklearn clusterers, refer to the [sklearn clustering with aeon distances](sklearn_clustering_with_aeon_distances.ipynb) notebook.\n",
+    "\n",
     "- Deep learning based TSCL is a very popular topic, and we are working on bringing\n",
     "deep learning functionality to `aeon`, first algorithms for [Deep learning] are\n",
     "COMING SOON\n",
@@ -55,13 +60,13 @@
     "\n",
     "<img src=\"img/clst_cd.png\" width=\"600\" alt=\"cd_diag\">\n",
     "\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   },
   {
    "cell_type": "markdown",
+   "metadata": {
+    "collapsed": false
+   },
    "source": [
     "## References\n",
     "\n",
@@ -141,10 +146,7 @@
     "[22] B. Lafabregue, J. Weber, P. Gancarski, and G. Forestier. End-to-end deep\n",
     "representation learning for time series clustering: a comparative study. Data Mining\n",
     "and Knowledge Discovery, 36:29—-81, 2022\n"
-   ],
-   "metadata": {
-    "collapsed": false
-   }
+   ]
   }
  ],
  "metadata": {

examples/distances/sklearn_distances.ipynb

@@ -763,7 +763,8 @@
     "collapsed": false
    },
    "source": [
-    "## Clustering with sklearn.cluster"
+    "## Clustering with sklearn.cluster\n",
+    "[Using aeon Distances with sklearn Clusterers](../clustering/sklearn_clustering_with_aeon_distances.ipynb)"
    ]
   },
   {