Integrating cluster formation and cluster evaluation in interactive visual analysis

Abstract

Cluster analysis is a popular method for data investigation where data items are structured into groups called clusters. This analysis involves two sequential steps, namely cluster formation and cluster evaluation. In this paper, we propose the tight integration of cluster formation and cluster evaluation in interactive visual analysis in order to overcome the challenges that relate to the black-box nature of clustering algorithms. We present our conceptual framework in the form of an interactive visual environment. In this realization of our framework, we build upon general concepts such as cluster comparison, clustering tendency, cluster stability and cluster coherence. Additionally, we showcase our framework on the cluster analysis of mixed lipid bilayers.

Interactive Clustering

“…The basic assumption made by the authors of the papers in this category is that an interactive and collaborative process combining the strengths of both human and machine would yield better results than a process that is purely automated or purely manual. Several examples of improving the quality of the clustering results using different strategies are given in the works presented in Andrienko and Andrienko [4], Basu et al [15], Boudjeloud-Assala et al [19], Cao et al [24], Castellanos-Garzón et al [26], Choo et al [30], Dobrynin et al [38], Hadlak et al [50], Hoque and Carenini [53], Hu et al [55], Kumpf et al [64], Lai et al [66], Lee et al [67], Lei et al [68], MacInnes et al [72], Packer et al [79], Schreck et al [86], Srivastava et al [94], Turkay et al [99,101], Zhou et al [116].…”

On Computationally-Enhanced Visual Analysis of Heterogeneous Data and Its Application in Biomedical Informatics

“…[51], [52], [53], [54], [55], [56] Groups & Classification [57] [58], [59] [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74] [75], [76], [77], [78], [79], [80] Dependence & Prediction [81], [82], [46] [83], [84], [85], [86], [87], [88], [89] [90], [91], [92] being analyzed. The results are then presented to the user through different visual encodings that are often accompanied by interaction.…”

“…Turkay et al presents an interactive system that addresses both the generation and evaluation stages in a clustering process [79]. Another example is the iVisClassifier by Choo et al [80] where the authors improve classifitcation performance through interactive visualizations.…”

StratomeX: Visual Analysis of Large‐Scale Heterogeneous Genomics Data for Cancer Subtype Characterization

“…Given a clean edge routing, ribbons make the distribution of records across different categories easy to follow and analyze. These techniques have been used extensively for subset comparison – for example, in CComViz [ZKG09], Matchmaker [LSP*10], Vis‐Bricks [LSS*11], and others [TPRH11].…”