Models of incremental concept formation
Artificial Intelligence
Graph drawing by force-directed placement
Software—Practice & Experience
A fast algorithm for local minimum and maximum filters on rectangular and octagonal kernels
Pattern Recognition Letters
An empirical study of algorithms for point-feature label placement
ACM Transactions on Graphics (TOG)
BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Concept decompositions for large sparse text data using clustering
Machine Learning
Information Retrieval: Computational and Theoretical Aspects
Information Retrieval: Computational and Theoretical Aspects
Clustering Algorithms
ThemeRiver: Visualizing Thematic Changes in Large Document Collections
IEEE Transactions on Visualization and Computer Graphics
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Incremental Singular Value Decomposition of Uncertain Data with Missing Values
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Incremental Clustering for Mining in a Data Warehousing Environment
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Incremental PCA or On-Line Visual Learning and Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
k-means++: the advantages of careful seeding
SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Tracking and modelling information diffusion across interactive online media
International Journal of Metadata, Semantics and Ontologies
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
Rapid and brief communication: Incremental locally linear embedding
Pattern Recognition
Incremental computation of information landscapes for dynamic web interfaces
Proceedings of the IX Symposium on Human Factors in Computing Systems
An incremental subspace learning algorithm to categorize large scale text data
APWeb'05 Proceedings of the 7th Asia-Pacific web conference on Web Technologies Research and Development
Incremental linear discriminant analysis for classification of data streams
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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Incrementally computed information landscapes are an effective means to visualize longitudinal changes in large document repositories. Resembling tectonic processes in the natural world, dynamic rendering reflects both long-term trends and short-term fluctuations in such repositories. To visualize the rise and decay of topics, the mapping algorithm elevates and lowers related sets of concentric contour lines. Addressing the growing number of documents to be processed by state-of-the-art knowledge discovery applications, we introduce an incremental, scalable approach for generating such landscapes. The processing pipeline includes a number of sequential tasks, from crawling, filtering and pre-processing Web content to projecting, labeling and rendering the aggregated information. Incremental processing steps are localized in the projection stage consisting of document clustering, cluster force-directed placement and fast document positioning. We evaluate the proposed framework by contrasting layout qualities of incremental versus non-incremental versions. Documents for the experiments stem from the blog sample of the Media Watch on Climate Change (www.ecoresearch.net/climate). Experimental results indicate that our incremental computation approach is capable of accurately generating dynamic information landscapes.