Can clustering be used for feature selection?

A novel clustering approach is proposed for feature selection from big data. The formation of clusters reduces the dimensionality and helps in selection of the relevant features for the target class.

How do you select best features for clustering?

How to do feature selection for clustering and implement it in…

1. Perform k-means on each of the features individually for some k.
2. For each cluster measure some clustering performance metric like the Dunn’s index or silhouette.
3. Take the feature which gives you the best performance and add it to Sf.

How do you use silhouette coefficients?

The Silhouette Coefficient is calculated using the mean intra-cluster distance ( a ) and the mean nearest-cluster distance ( b ) for each sample. The Silhouette Coefficient for a sample is (b – a) / max(a, b) . To clarify, b is the distance between a sample and the nearest cluster that the sample is not a part of.

What do you understand by Silhouette coefficient?

Silhouette Coefficient or silhouette score is a metric used to calculate the goodness of a clustering technique. Its value ranges from -1 to 1. 1: Means clusters are well apart from each other and clearly distinguished. b= average inter-cluster distance i.e the average distance between all clusters.

What does a silhouette value of indicate?

The silhouette value is a measure of how similar an object is to its own cluster (cohesion) compared to other clusters (separation). The silhouette ranges from −1 to +1, where a high value indicates that the object is well matched to its own cluster and poorly matched to neighboring clusters.

What are the variables that are considered to create the clusters by default?

By default, Tableau created the clusters from the variables on the view (Sales and Profit Ratio). You can add or take away variables to customize the clusters. Clusters were added to the Color Marks Card, which colored each circle by its respective cluster segment.