Exploratory Visualization for Association Rule Rummaging

[1]  Henry Montgomery,et al.  Decision Rules and the Search for a Dominance Structure: Towards a Process Model of Decision Making* , 1983 .

[2]  B. Shneiderman,et al.  The dynamic HomeFinder: evaluating dynamic queries in a real-estate information exploration system , 1992, SIGIR '92.

[3]  Inderpal Bhandari,et al.  Attribute focusing: machine-assisted knowledge discovery applied to software production process control , 1993 .

[4]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[5]  Stephen C. North,et al.  Incremental Layout in DynaDAG , 1995, GD.

[6]  K. Andrews,et al.  Case study. Visualising cyberspace: information visualisation in the Harmony Internet browser , 1995, Proceedings of Visualization 1995 Conference.

[7]  Heikki Mannila,et al.  Interactive Exploration of Discovered Knowledge: A Methodology for Interaction, and Usability Studie , 1996 .

[8]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[9]  Heikki Mannila,et al.  A database perspective on knowledge discovery , 1996, CACM.

[10]  Tim Bray,et al.  Measuring the Web , 1996, World Wide Web J..

[11]  Matthew Chalmers,et al.  Adding imageability features to information displays , 1996, UIST '96.

[12]  Georges G. Grinstein.  Harnessing the Human in Knowledge Discovery , 1996, KDD.

[13]  Gang Liu,et al.  DBMiner: a system for data mining in relational databases and data warehouses , 1997, CASCON.

[14]  Einoshin Suzuki,et al.  Autonomous Discovery of Reliable Exception Rules , 1997, KDD.

[15]  Pak Chung Wong,et al.  Visualizing association rules for text mining , 1999, Proceedings 1999 IEEE Symposium on Information Visualization (InfoVis'99).

[16]  Wynne Hsu,et al.  Pruning and summarizing the discovered associations , 1999, KDD '99.

[17]  Nick Cercone,et al.  RuleViz: a model for visualizing knowledge discovery process , 2000, KDD '00.

[18]  Fabrice Guillet,et al.  A User-Driven Process for Mining Association Rules , 2000, PKDD.

[19]  Ivan Herman,et al.  Graph Visualization and Navigation in Information Visualization: A Survey , 2000, IEEE Trans. Vis. Comput. Graph..

[20]  Nick Cercone,et al.  AViz: A Visualization System for Discovering Numeric Association Rules , 2000, PAKDD.

[21]  Wynne Hsu,et al.  Multi-level organization and summarization of the discovered rules , 2000, KDD '00.

[22]  John F. Roddick,et al.  Visualisation of Temporal Interval Association Rules , 2000, IDEAL.

[23]  Heike Hofmann,et al.  Visual Comparison of Association Rules , 2001, Comput. Stat..

[24]  Howard J. Hamilton,et al.  Knowledge discovery and measures of interest , 2001 .

[25]  Andreas Wierse,et al.  Information Visualization in Data Mining and Knowledge Discovery , 2001 .

[26]  Yasuhiko Morimoto,et al.  Data Mining with optimized two-dimensional association rules , 2001, TODS.

[27]  Markus H. Gross,et al.  Visualization of directed associations in e-commerce transaction data , 2001, VisSym.

[28]  Erik Granum,et al.  Methods for visual mining of data in Virtual Reality , 2001 .

[29]  Jaideep Srivastava,et al.  Selecting the right interestingness measure for association patterns , 2002, KDD.

[30]  Ee-Peng Lim,et al.  CrystalClear: Active visualization of association rules , 2002 .

[31]  Daniel A. Keim,et al.  Information Visualization and Visual Data Mining , 2002, IEEE Trans. Vis. Comput. Graph..

[32]  Charu C. Aggarwal,et al.  Towards effective and interpretable data mining by visual interaction , 2002, SKDD.

[33]  Régis Gras,et al.  Implication Intensity: From the Basic Statistical Definition to the Entropic Version , 2003 .

[34]  Fabrice Guillet,et al.  A virtual Reality Environment for Knowledge Mining , 2003 .