Both supervised and unsupervised learning methods share common concerns with respect to noisy data, high dimensionality, and demands on memory and time as the size of data grows. Other issues peculiar to unsupervised learning, due to the lack of ground truth, are questions relating to subjectivity in the evaluation of models and their interpretability, effect of cluster boundaries, and so on.
Feature reduction is an important preprocessing step that mitigates the scalability problem, in addition to presenting other advantages. Linear methods such as PCA, Random Projection, and MDS, each have specific benefits and limitations, and we must be aware of the assumptions inherent in each. Nonlinear feature reduction methods include KPCA and Manifold learning.
Among clustering algorithms, k-Means is a centroid-based technique initialized by selecting the number of clusters and it is sensitive to the initial choice of centroids. DBSCAN is one of the density-based algorithms that does not need...