We have carried out the clustering of a dataset using Kmeasn algorithams
We are not sure what all parameters are required to calculate which helps us validate how good/bad our clusters are created in Machine learning?
Is Accuracy a good parameter for clustering validation?
Please guide us.
Thank you.
In addition to accuracy, Silhouette coefficient and Dunn index are used to assess the effectiveness of clustering algorithms. The Silhouette Score calculates the separation between clusters which shows distance between each point in a cluster and points in other clusters. This metric ranges from -1 to 1, and when the silhouette coefficient is high, the data points are tightly clustered; when it is low, the data points might not be in the right cluster. The Dunn index measures the ratio of inter-cluster distance to intra-cluster distance. Larger the distances between clusters (better separation) and smaller cluster sizes (more compact clusters) lead to higher DI values. A higher DI indicates a better clustering.
When I performed a K-mean clustering, I did a priori silhouette score on R software to determine the optimal number of clusters when the silhouette score is the highest (thanks to the function fviz_nbclust in the package factoextra). The silhouette score is an efficient measure because it takes account into both the separation (i.e. how far two observations of different clusters are) and the cohesion (i.e., how close two observations of the same cluster are). After I ran the algorithm and I visualised it with spider plots trying to find a label the closest to reality and if i had factors of repartition (for example age, gender), I used chi-squared tests to investigate if there is a link between cluster membership and these factors. I used this unsupervised technic generally for data exploration and i completed the data analysis with supervised machine learning.
Validating the quality of clusters generated by clustering algorithms, such as K-means, in unsupervised learning can be done using various evaluation metrics. Here are some common methods for cluster validation:
It's important to note that different evaluation metrics may be more suitable for specific datasets and clustering algorithms. Also, validation metrics should not be the sole basis for assessing cluster quality. Expert knowledge and domain-specific considerations should also be taken into account.
We can be Validating the quality of clusters created by clustering algorithms is an important step in unsupervised learning. There are several methods you can use to evaluate the performance of clustering algorithms such as K-means. Here are a few commonly used techniques:
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