🧵1/9 Let's talk about methods for identifying the optimal number of clusters in cluster analysis!
Cluster analysis is a technique used to group data points based on their similarity. Here are some popular methods & R packages. #RStats #DataScience
Cluster analysis is a technique used to group data points based on their similarity. Here are some popular methods & R packages. #RStats #DataScience
🔍2/9 Elbow Method: The Elbow Method involves plotting the explained variation (inertia) as a function of the number of clusters. The "elbow point" on the curve represents the optimal number of clusters. R package: 'factoextra' #RStats #DataScience cran.r-project.org
📈3/9 Silhouette Score: This method evaluates the quality of clustering by calculating the average silhouette score of each data point. Higher silhouette scores indicate better cluster assignments. Optimal clusters have the highest average silhouette score.cran.r-project.org
🌈4/9 Gap Statistic: The Gap Statistic method compares the total within-cluster sum of squares (WSS) with that of a null reference distribution. The optimal number of clusters is found when the gap is the largest. R package: 'cluster' and 'factoextra' #RStats #DataScience
🎯5/9 Calinski-Harabasz (CH) Index: The CH Index compares the between-cluster sum of squares (BSS) to the within-cluster sum of squares (WSS) for different numbers of clusters. The optimal number of clusters has the highest CH Index value. R package: 'cluster' #RStats
💡6/9 Consensus Clustering: This method combines multiple clustering runs with varying numbers of clusters to identify the most stable partitioning of the data. The optimal number of clusters is chosen based on the stability of the partitions. bioconductor.org
🧠7/9 Bayesian Information Criterion (BIC) & Akaike Information Criterion (AIC): Both BIC & AIC are model selection criteria that help identify the optimal number of clusters by balancing the model complexity and goodness-of-fit. #RStats #DataScience cran.r-project.org
🧪8/9 Bonus: If you are using hierarchical clustering methods, you can use the 'dynamicTreeCut' R package to find the optimal number of clusters by cutting the dendrogram at the most suitable height. #RStats #DataScience cran.r-project.org
🔚9/9 There are many methods to determine the optimal number of clusters, and each has its pros and cons. It's essential to consider the specific application and data when choosing a method. Stay curious and keep exploring! 🚀 #DataScience #ClusterAnalysis #Rstats
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