Visual Mining of Association Rules

Association Rules are one of the most widespread data mining tools because they can be easily mined, even from very huge database, and they provide valuable information for many application fields such as marketing, credit scoring, business, etc. The counterpart is that a massive effort is required (due to the large number of rules usually mined) in order to make actionable the retained knowledge. In this framework vizualization tools become essential to have a deep insight into the association structures and interactive features have to be exploited for highlighting the most relevant and meaningful rules.

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

[2]  Dario Bruzzese,et al.  Combining visual techniques for Association Rules exploration , 2004, AVI.

[3]  Laks V. S. Lakshmanan,et al.  Interestingness and Pruning of Mined Patterns , 1999, 1999 ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[4]  Alfred Inselberg.  Visual Data Mining with Parallel Coordinates , 1998 .

[5]  J. A. Hartigan,et al.  Mosaics for Contingency Tables , 1981 .

[6]  Yiming Ma,et al.  Analyzing the interestingness of association rules from the temporal dimension , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

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

[8]  Geoffrey I. Webb,et al.  Preliminary investigations into statistically valid exploratory rule discovery , 2003 .

[9]  D. Madigan,et al.  Proceedings : KDD-99 : the Fifth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August 15-18, 1999, San Diego, California, USA , 1999 .

[10]  Christine L. Lisetti,et al.  Proceedings of the 13th International Conference on Human Computer Interaction , 2009 .

[11]  Heikki Mannila,et al.  Pruning and grouping of discovered association rules , 1995 .

[12]  Nimrod Megiddo,et al.  Discovering Predictive Association Rules , 1998, KDD.

[13]  Daniel A. Keim,et al.  43 Visual Data-Mining Techniques* , 2004 .

[14]  H. Hofmann.  Exploring categorical data: interactive mosaic plots , 2000 .

[15]  Li Yang,et al.  Visualizing Frequent Itemsets, Association Rules, and Sequential Patterns in Parallel Coordinates , 2003, ICCSA.

[16]  Dario Bruzzese,et al.  Parallel Coordinates for Interactive Exploration of Association Rules , 2003 .

[17]  Heikki Mannila,et al.  Finding interesting rules from large sets of discovered association rules , 1994, CIKM '94.

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

[19]  Antony Unwin,et al.  The TwoKey Plot for Multiple Association Rules Control , 2001, PKDD.

[20]  Jan Komorowski,et al.  Principles of Data Mining and Knowledge Discovery , 2001, Lecture Notes in Computer Science.

[21]  J. G. Bethlehem,et al.  COMPSTAT 2000, Proceedings in Computational Statistics , 2000 .

[22]  Marina L. Gavrilova,et al.  Computational Science and Its Applications — ICCSA 2003 , 2003 .

[23]  M. Greenacre.  Correspondence analysis in practice , 1993 .

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

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

[26]  Heike Hofmann,et al.  Visualizing association rules with interactive mosaic plots , 2000, KDD '00.

[27]  Padhraic Smyth,et al.  Statistical inference and data mining , 1996, CACM.

[28]  Heike Hofmann,et al.  Validation of association rules by interactive mosaic plots , 2000 .