BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
An assessment of finite sample performance of adaptive methods in density estimation
Computational Statistics & Data Analysis
The data webhouse toolkit: building the web-enabled data warehouse
The data webhouse toolkit: building the web-enabled data warehouse
The K-D-B-tree: a search structure for large multidimensional dynamic indexes
SIGMOD '81 Proceedings of the 1981 ACM SIGMOD international conference on Management of data
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
Optimal Grid-Clustering: Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
A Modulated Parzen-Windows Approach for Probability Density Estimation
IDA '97 Proceedings of the Second International Symposium on Advances in Intelligent Data Analysis, Reasoning about Data
STING: A Statistical Information Grid Approach to Spatial Data Mining
VLDB '97 Proceedings of the 23rd International Conference on Very Large Data Bases
H-BLOB: A Hierarchical Visual Clustering Method Using Implicit Surfaces
VISUALIZATION '00 Proceedings of the 11th IEEE Visualization 2000 Conference (VIS 2000)
Visualizing Informationon a Sphere
INFOVIS '97 Proceedings of the 1997 IEEE Symposium on Information Visualization (InfoVis '97)
GGobi: evolving from XGobi into an extensible framework for interactive data visualization
Computational Statistics & Data Analysis - Data visualization
Introduction to Data Mining Using SAS Enterprise Miner
Introduction to Data Mining Using SAS Enterprise Miner
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Clickstreams are among the most popular data sources because Web servers automatically record each action and the Web log entries promise to add up to a comprehensive description of behaviors of users. Clickstreams, however, are large and raise a number of unique challenges with respect to visual data mining. At the technical level the huge amount of data requires scalable solutions and limits the presentation to summary and model data. Equally challenging is the interpretation of the data at the conceptual level. Many analysis tools are able to produce different types of statistical charts. However, the step from statistical charts to comprehensive information about customer behavior is still largely unresolved. We propose a density surface based analysis of 3D data that uses state-of-the-art interaction techniques to explore the data at various granularities.