Integrating cluster formation and cluster evaluation in interactive visual analysis

Authors:
Cagatay Turkay;Julius Parulek;Nathalie Reuter;Helwig Hauser
Affiliations:
University of Bergen;University of Bergen;BCCS, University of Bergen;University of Bergen
Venue:
Proceedings of the 27th Spring Conference on Computer Graphics
Year:
2011

Citing 15
Cited 1

H-BLOB: a hierarchical visual clustering method using implicit surfaces

Proceedings of the conference on Visualization '00
Interactively Exploring Hierarchical Clustering Results

Computer
High Dimensional Brushing for Interactive Exploration of Multivariate Data

VIS '95 Proceedings of the 6th conference on Visualization '95
Stability-based validation of clustering solutions

Neural Computation
Interactive Cluster Analysis

IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Introduction to Data Mining, (First Edition)

Introduction to Data Mining, (First Edition)
Parallel Sets: Interactive Exploration and Visual Analysis of Categorical Data

IEEE Transactions on Visualization and Computer Graphics
Interactive Visual Analysis of Families of Function Graphs

IEEE Transactions on Visualization and Computer Graphics
Heat Map Visualizations Allow Comparison of Multiple Clustering Results and Evaluation of Dataset Quality: Application to Microarray Data

IV '07 Proceedings of the 11th International Conference Information Visualization
Visual Verification and Analysis of Cluster Detection for Molecular Dynamics

IEEE Transactions on Visualization and Computer Graphics
Vectorized Radviz and Its Application to Multiple Cluster Datasets

IEEE Transactions on Visualization and Computer Graphics
Visually driven analysis of movement data by progressive clustering

Information Visualization
Integrating Data Clustering and Visualization for the Analysis of 3D Gene Expression Data

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Data clustering: 50 years beyond K-means

Pattern Recognition Letters
Comparative Analysis of Multidimensional, Quantitative Data

IEEE Transactions on Visualization and Computer Graphics

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

Computer Graphics Forum

Quantified Score

Hi-index	0.00

Visualization

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.