Recovering the number of clusters in data sets with noise features using feature rescaling factors
This work addresses a specific challenge in clustering analysis for data with irrelevant features, but it is incremental as it builds on existing validity indexes.
The paper tackles the problem of estimating the true number of clusters in data sets with noise features by introducing three feature rescaling methods, which increase the likelihood of clustering validity indexes returning accurate counts.
In this paper we introduce three methods for re-scaling data sets aiming at improving the likelihood of clustering validity indexes to return the true number of spherical Gaussian clusters with additional noise features. Our method obtains feature re-scaling factors taking into account the structure of a given data set and the intuitive idea that different features may have different degrees of relevance at different clusters. We experiment with the Silhouette (using squared Euclidean, Manhattan, and the p$^{th}$ power of the Minkowski distance), Dunn's, Calinski-Harabasz and Hartigan indexes on data sets with spherical Gaussian clusters with and without noise features. We conclude that our methods indeed increase the chances of estimating the true number of clusters in a data set.