Concept decompositions for large sparse text data using clustering
Machine Learning
ThemeRiver: Visualizing Thematic Changes in Large Document Collections
IEEE Transactions on Visualization and Computer Graphics
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
The InfoSky visual explorer: exploiting hierarchical structure and document similarities
Information Visualization
Visualizing time-oriented data-A systematic view
Computers and Graphics
Fused Exploration of Temporal Developments and Topical Relationships in Heterogeneous Data Sets
IV '07 Proceedings of the 11th International Conference Information Visualization
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Computational Geometry: Algorithms and Applications
Computational Geometry: Algorithms and Applications
Multiple coordinated views for searching and navigating Web content repositories
Information Sciences: an International Journal
Visual Knowledge Discovery in Dynamic Enterprise Text Repositories
IV '09 Proceedings of the 2009 13th International Conference Information Visualisation
Automatic Cluster Number Selection Using a Split and Merge K-Means Approach
DEXA '09 Proceedings of the 2009 20th International Workshop on Database and Expert Systems Application
Scalable annotation mechanisms for digital content aggregation and context-aware authoring
Proceedings of the 10th Brazilian Symposium on on Human Factors in Computing Systems and the 5th Latin American Conference on Human-Computer Interaction
Extraction and interactive exploration of knowledge from aggregated news and social media content
Proceedings of the 4th ACM SIGCHI symposium on Engineering interactive computing systems
Dynamic topography information landscapes: an incremental approach to visual knowledge discovery
DaWaK'12 Proceedings of the 14th international conference on Data Warehousing and Knowledge Discovery
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This paper presents a technique for the visual analysis of topical shifts in dynamically changing textual archives. Our approach is based on the well-known information landscape metaphor, whereby topical changes are represented by changes in landscape topography. Incremental clustering and multi-dimensional scaling algorithms are periodically applied to a changing document set for generating a series of information landscapes. The resulting landscapes are suitable for dynamic Web interfaces, enabling the user to explore topical relationships and understand topical shifts and trends in changing document repositories.