Your choice of cluster analysis algorithm is important, particularly when you have mixed data. In major statistics packages you’ll find a range of preset algorithms ready to number-crunch your matrices. K-means and K-medoid are two of the most suitable clustering methods. In both cases (K)...
we focus on clustering unlabeled sets of feature vectors. To cluster those objects, the common approach so far is to select some distance measures for point sets like [6, 7] and then apply a distance-based clustering algorithm e.g. k-medoid methods like CLARANS [8] or a density-based al...
Clustering is an unsupervised machine learning algorithm that organizes and classifies different objects, data points, or observations into groups or clusters based on similarities or patterns.
Cluster analysis can be a powerful data-mining tool to identify discrete groups of customers, sales transactions, or types of behaviours.
Learn everything you need to know about cluster analysis: Definition ✓ How it is used ✓ Basic questions ✓Cluster analysis + factor analysis ✓