Data mining for association rules and sequential patterns: sequential and parallel algorithms
Data mining for association rules and sequential patterns: sequential and parallel algorithms
Fast ordering of large categorical datasets for better visualization
Proceedings of the seventh 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
Mapping nominal values to numbers for effective visualization
Information Visualization - Special issue of selected and extended InfoVis 03 papers
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
Finding hierarchical heavy hitters in data streams
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
Analysis and Interpretation of Visual Hierarchical Heavy Hitters of Binary Relations
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
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This chapter presents VHHH: a visual data mining tool to compute and investigate hierarchical heavy hitters (HHHs) for two-dimensional data. VHHH computes the HHHs for a two-dimensional categorical dataset and a given threshold, and visualizes the HHHs in the three dimensional space. The chapter evaluates VHHH on synthetic and real world data, provides an interpretation alphabet, and identifies common visualization patterns of HHHs.