diff --git a/Excalidraw/ClusteringBasic.excalidraw.md b/Excalidraw/ClusteringBasic.excalidraw.md new file mode 100644 index 0000000..8c214ba --- /dev/null +++ b/Excalidraw/ClusteringBasic.excalidraw.md @@ -0,0 +1,196 @@ +--- + +excalidraw-plugin: parsed +tags: [excalidraw] + +--- +==⚠ Switch to EXCALIDRAW VIEW in the MORE OPTIONS menu of this document. ⚠== You can decompress Drawing data with the command palette: 'Decompress current Excalidraw file'. For more info check in plugin settings under 'Saving' + + +# Excalidraw Data + +## Text Elements +%% +## Drawing +```compressed-json +N4KAkARALgngDgUwgLgAQQQDwMYEMA2AlgCYBOuA7hADTgQBuCpAzoQPYB2KqATLZMzYBXUtiRoIACyhQ4zZAHoFAc0JRJQgEYA6bGwC2CgF7N6hbEcK4OCtptbErHALRY8RMpWdx8Q1TdIEfARcZgRmBShcZR5tHgBmbXiaOiCEfQQOKGZuAG1wMFAwYogSbggANgrmDgBhABl4gEcU4shYRHLA7CiOZWDWksxuZ3ieCu0ABh4AdniAFniZgFZ+ + +EpgRhPntAEZlip5VgsgKEnVuCuWATm0ZnYAOFbXISQRCZWluZcmbnavl+I7Q7PCDWfriVCTEHMKCkNgAawQtTY+DYpHKAGIdghsdjBpBNLhsPDlHChBxiMjUeiJLDrMw4LhAll8RAAGaEfD4ADKsAGEkEHlZMLhiIA6mdJNw+McICKEQheTB+ehBWUQWSPhxwjk0DsQWxGdg1Bs9ZMobLScI4ABJYi61C5AC6ILZ5Aydu4HCEXJBhApWHKuEmrLJ + +FO1zAdRTa0HgEPixwAvtCEAhiNw/jN5vcruN4haY4wWOwuGgZjMQUXWJwAHKcMQZlY7eazHg7AslQjMAAiaSgae4bIIYRBmmEFIAosEMlkHc6QUI4MRcP303qZvdzQ9lvMKld5lcQUQOPCvT78Ee2MSB2gh/gR7LJKEACpYKD1f2n2/DhAFZMFaNIDKCRnBmABZepSAnblswAVRtZ9agoZZ4XUTB4jgVkOghdlAjTcgqBBYY0GcHgEm0K4ZnGfZg + 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100644 --- a/Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md +++ b/Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md @@ -6,4 +6,16 @@ - clustering - groups instances based on feature similarity - results in group assignments, not target output - - \ No newline at end of file + - need to extrapolate meaning from clusters + - ![[ClusteringBasic.excalidraw]] + - applications + - taxonomy of living things + - clustering documents on topic + - identify areas with similar land use + - cluster groups of houses for city planning + - good clustering: high intra-class similarity, low inter-class similarity + - centroid + - mean position of a cluster's instances $$\overline X_{i}=\frac{\sum_{j \in C_{i}}X_{i}}{n_{i}}$$ + - Inertia + - aerage squared distance of the instances from the centroid $$I_{i}=\frac{\sum_{j \in C_{i}}|\overline X_{j} - X_{i}|^2}{n_{i}}$$ + - \ No newline at end of file