diff --git a/.obsidian/plugins/harper/data.json b/.obsidian/plugins/harper/data.json index a61a299..5950b49 100644 --- a/.obsidian/plugins/harper/data.json +++ b/.obsidian/plugins/harper/data.json @@ -1,5 +1,5 @@ { - "ignoredLints": "{\"context_hashes\":[15865895689566000533,6119222255552956109,11253296314840191199,16721049695819994954,8216002975862858808,214610641060740680,9548152354067699590]}", + "ignoredLints": "{\"context_hashes\":[11253296314840191199,9548152354067699590,16721049695819994954,6119222255552956109,15865895689566000533,214610641060740680,8216002975862858808]}", "useWebWorker": true, "lintSettings": { "ACoupleMore": null, @@ -434,6 +434,8 @@ "frc", "hea150", "math163", + "medioid", + "medioids", "parallelpiped", "pcb" ], diff --git a/Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md b/Running Start/CSB320 - Machine Learning Concepts/Class 5-28 (Clustering).md index e9f7b95..64293c0 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 @@ -36,4 +36,17 @@ - $\overline{d_{out}(j)}$ is the average distance of the instance $i$ to the centroid of all other clusters - $\overline{d_{in}(j)}$ is the average distance of the instance $i$ to all other instances in its cluster - ![[SilhouetteMethod.excalidraw]] - - \ No newline at end of file + - effective for small/medium datasets + - assumes spherical clusters + - sensitive to initial conditions + - equal-sized groups assumption + - sensitive to outliers + - k-medioids can be used + - medioid is the most centrally located point in a cluster + - PAM: partitioning around medioids + - Hierarchical clustering + - does not require number of clusters, requires stopping point + - Agglomerative + - starts with each instance as cluster and merges into larger clusters + - divisive + - starts with one cluster and splits into \ No newline at end of file