cluster analysis post final

_posts/2024-01-11-Overview-clustering-methods-in-R.md

@@ -4,7 +4,7 @@ author: "Peter Laurinec"
 layout: post
 published: true
 status: publish
-tags: clustering
+tags: R clustering
 draft: no
 ---
 

_rmd/2024-01-11-Overview-clustering-methods-in-R.Rmd

@@ -3,8 +3,8 @@ title: "Overview of clustering methods in R"
 author: "Peter Laurinec"
 layout: post
 published: no
-status: process
-tags: clustering
+status: processed
+tags: R clustering
 draft: no
 ---
 
@@ -233,7 +233,7 @@ The Elbow diagram is a simple method (rule) how to determine the number of clust
 
 So for example, I chose the Davies-Bouldin index implemented in the `clusterCrit` package. For our simple dataset, I will generate clusterings with 2-6 number of clusters and compute the index.
 
-```{r, fig.width=8, fig.height=6.5, warning=FALSE}
+```{r, fig.width=8, fig.height=5.5, warning=FALSE}
 library(clusterCrit)
 
 km_res_k <- lapply(2:6, function(i) kmeans(data_example[, .(x, y)], i)$cluster)
@@ -263,7 +263,7 @@ We can see that the Elbow diagram rule chose 4 clusters - makes sense to me actu
 
 We can also try it with PAM - K-medoids.
 
-```{r, fig.width=8, fig.height=6.5}
+```{r, fig.width=8, fig.height=5.5}
 kmed_res_k <- lapply(2:6, function(i) pam(data_example[, .(x, y)], i)$clustering)
 
 db_kmed <- lapply(kmed_res_k, function(j) intCriteria(data.matrix(data_example[, .(x, y)]),
@@ -316,7 +316,7 @@ Pros and cons:
 
 EM algorithm with GMM is implemented in the `mclust` package. You can optimize various shapes of mixtures (clusters) by the `modelNames` parameter (check the `?mclustModelNames` function for more details).
 
-```{r, message=FALSE, warning=FALSE, fig.height=8, fig.width=6.5}
+```{r, message=FALSE, warning=FALSE, fig.height=6.5, fig.width=8}
 library(mclust)
 res <- Mclust(data_example[, .(x, y)], G = 3, modelNames = "VVV", verbose = FALSE)
 
@@ -339,7 +339,7 @@ plot(res, what = "BIC")
 ```
 
 The result:
-```{r, fig.height=8, fig.width=6.5}
+```{r, fig.height=6.5, fig.width=8}
 plot(res, what = "classification")
 ```