Data mining for association rules and sequential patterns: sequential and parallel algorithms
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Visualizing Categorical Data
IEEE Transactions on Visualization and Computer Graphics
Query, analysis, and visualization of hierarchically structured data using Polaris
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Visualizing Association Rules for Text Mining
INFOVIS '99 Proceedings of the 1999 IEEE Symposium on Information Visualization
Modern Data Warehousing, Mining, and Visualization: Core Concepts
Modern Data Warehousing, Mining, and Visualization: Core Concepts
Visualization of association rules over relational DBMSs
Proceedings of the 2003 ACM symposium on Applied computing
Diamond in the rough: finding Hierarchical Heavy Hitters in multi-dimensional data
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Extending the Utility of Treemaps with Flexible Hierarchy
IV '04 Proceedings of the Information Visualisation, Eighth International Conference
Online identification of hierarchical heavy hitters: algorithms, evaluation, and applications
Proceedings of the 4th ACM SIGCOMM conference on Internet measurement
Space complexity of hierarchical heavy hitters in multi-dimensional data streams
Proceedings of the twenty-fourth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Parallel Sets: Visual Analysis of Categorical Data
INFOVIS '05 Proceedings of the Proceedings of the 2005 IEEE Symposium on Information Visualization
Exploring OLAP aggregates with hierarchical visualization techniques
Proceedings of the 2007 ACM symposium on Applied computing
Finding hierarchical heavy hitters in data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Using 2D Hierarchical Heavy Hitters to Investigate Binary Relationships
Visual Data Mining
From analysis to interactive exploration: building visual hierarchies from OLAP cubes
EDBT'06 Proceedings of the 10th international conference on Advances in Database Technology
Entity timelines: visual analytics and named entity evolution
Proceedings of the 20th ACM international conference on Information and knowledge management
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The emerging field of visual analytics changes the way we model, gather, and analyze data. Current data analysis approaches suggest to gather as much data as possible and then focus on goal and process oriented data analysis techniques. Visual analytics changes this approach and the methodology to interpret the results becomes the key issue.This paper contributes with a method to interpret visual hierarchical heavy hitters (VHHHs). We show how to analyze data on the general level and how to examine specific areas of the data. We identify five common patterns that build the interpretation alphabet of VHHHs. We demonstrate our method on three different real world datasets and show the effectiveness of our approach.