Ensemble methods that train multiple learners and then combine their predictions have been shown to be very effective in supervised learning. This paper explores ensemble methods ...
In this paper, we bring out a new density-based clustering initialization algorithm which is invariant to the scale factor. Instead of using the scale factor while the cluster ini...
The goal of any clustering algorithm is to find the optimal clustering solution with the optimal number of clusters. In order to evaluate a clustering solution, a number of validit...
K-means is a widely used partitional clustering method. While there are considerable research efforts to characterize the key features of K-means clustering, further investigation...
Background: Hierarchical clustering is a widely applied tool in the analysis of microarray gene expression data. The assessment of cluster stability is a major challenge in cluste...