An algorithm for drawing general undirected graphs
Information Processing Letters
Graph drawing by force-directed placement
Software—Practice & Experience
A spectral algorithm for envelope reduction of sparse matrices
Proceedings of the 1993 ACM/IEEE conference on Supercomputing
Galaxy of news: an approach to visualizing and understanding expansive news landscapes
UIST '94 Proceedings of the 7th annual ACM symposium on User interface software and technology
TileBars: visualization of term distribution information in full text information access
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Drawing graphs nicely using simulated annealing
ACM Transactions on Graphics (TOG)
Space-efficient approximate Voronoi diagrams
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
On Clustering Validation Techniques
Journal of Intelligent Information Systems
ThemeRiver: Visualizing Thematic Changes in Large Document Collections
IEEE Transactions on Visualization and Computer Graphics
Performance criteria for graph clustering and Markov cluster experiments
Performance criteria for graph clustering and Markov cluster experiments
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Relationship-Based Clustering and Visualization for High-Dimensional Data Mining
INFORMS Journal on Computing
Visualization-enabled multi-document summarization by Iterative Residual Rescaling
Natural Language Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
VISTA: validating and refining clusters via visualization
Information Visualization
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
A new Mallows distance based metric for comparing clusterings
ICML '05 Proceedings of the 22nd international conference on Machine learning
Active learning via transductive experimental design
ICML '06 Proceedings of the 23rd international conference on Machine learning
The Relationships Among Various Nonnegative Matrix Factorization Methods for Clustering
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
IEEE Transactions on Knowledge and Data Engineering
Semi-supervised visual clustering for spherical coordinates systems
Proceedings of the 2008 ACM symposium on Applied computing
Jigsaw: supporting investigative analysis through interactive visualization
Information Visualization
The Word Tree, an Interactive Visual Concordance
IEEE Transactions on Visualization and Computer Graphics
Document Visualization Based on Semantic Graphs
IV '09 Proceedings of the 2009 13th International Conference Information Visualisation
Exemplar-based Visualization of Large Document Corpus (InfoVis2009-1115)
IEEE Transactions on Visualization and Computer Graphics
Interactive, topic-based visual text summarization and analysis
Proceedings of the 18th ACM conference on Information and knowledge management
GAP: A graphical environment for matrix visualization and cluster analysis
Computational Statistics & Data Analysis
Visual summarization of web pages
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
VisualSum: an interactive multi-document summarizationsystem using visualization
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
iDVS: an interactive multi-document visual summarization system
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part III
Online spatial data analysis and visualization system
Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
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Over the last decade, document clustering, as one of the key tasks in information organization and navigation, has been widely studied. Many algorithms have been developed for addressing various challenges in document clustering and for improving clustering performance. However, relatively few research efforts have been reported on evaluating and understanding document clustering results. In this article, we present DClusterE, a comprehensive and effective framework for document clustering evaluation and understanding using information visualization. DClusterE integrates cluster validation with user interactions and offers rich visualization tools for users to examine document clustering results from multiple perspectives. In particular, through informative views including force-directed layout view, matrix view, and cluster view, DClusterE provides not only different aspects of document inter/intra-clustering structures, but also the corresponding relationship between clustering results and the ground truth. Additionally, DClusterE supports general user interactions such as zoom in/out, browsing, and interactive access of the documents at different levels. Two new techniques are proposed to implement DClusterE: (1) A novel multiplicative update algorithm (MUA) for matrix reordering to generate narrow-banded (or clustered) nonzero patterns from documents. Combined with coarse seriation, MUA is able to provide better visualization of the cluster structures. (2) A Mallows-distance-based algorithm for establishing the relationship between the clustering results and the ground truth, which serves as the basis for coloring schemes. Experiments and user studies are conducted to demonstrate the effectiveness and efficiency of DClusterE.