Clustering becomes difficult due to the increasing sparsity of such data, as well as the increasing difficulty in distinguishing distances between data points. The proposed method called “kernel trick” and “Collective Neighbour Clustering”, which takes as input measures of correspondence between pairs of data points. Real-valued hubs are exchanged between data points until a high-quality set of patterns and corresponding clusters gradually emerges (Aggarwal et al., 2015). To validate our theory by demonstrating that hubness is a high-quality measure of point centrality within a high dimensional information cluster, and by proposing several hubness-based clustering algorithms, showing that main hubs can be used effectively as cluster prototypes or as guides during the search for centroid-based cluster patterns (Gnanabaskaran et al., 2011). Experimental results demonstrate the good performance of our proposed algorithms in manifold settings, mainly focused on large quantities of overlapping noise. The proposed methods are modified mostly for detecting approximately hyper spherical clusters and need to be extended to properly handle clusters of arbitrary shapes (Naveen et al., 2011). For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose systematization based on a thorough literature review. We carefully analyze the papers and derive a set of factors for discriminating the quality metrics, visualization techniques, and the process itself (David, 2010). The process is described through a reworked version of the well-known information visualization pipeline. We demonstrate the usefulness of our model by applying it to several existing approaches that use quality metrics, and we provide reflections on implications of our model for future research. High-dimensional data arise naturally in many domains, and have regularly presented a great challenge for traditional data-mining techniques, both in terms of effectiveness and efficiency (Sembiring et al., 2010). Clustering becomes difficult due to the increasing sparsity of such data, as well as the increasing difficulty in distinguishing distances between data points. In this paper we take a novel perspective on the problem of clustering high-dimensional data (Singh, 2012). Instead of attempting to avoid the curse of dimensionality by observing a lower-dimensional feature subspace, we embrace dimensionality by taking advantage of some inherently high-dimensional phenomena. More specifically, we show that hubness, i.e., the tendency of high-dimensional data to contain points (hubs) that frequently occur in k-nearest neighbour lists of other points, can be successfully exploited in clustering. We validate our hypothesis by proposing several hubness-based clustering algorithms and testing them on high-dimensional data. Experimental results demonstrate good performance of our algorithms in multiple settings, particularly in the presence of large quantities of noise (Mohamed et al., 2009).
