DocumentCode :
1175939
Title :
Pruning and visualizing generalized association rules in parallel coordinates
Author :
Yang, Li
Author_Institution :
Dept. of Comput. Sci., Western Michigan Univ., Kalamazoo, MI, USA
Volume :
17
Issue :
1
fYear :
2005
Firstpage :
60
Lastpage :
70
Abstract :
One fundamental problem for visualizing frequent itemsets and association rules is how to present a long border of frequent itemsets in an itemset lattice. Another problem comes from the lack of an effective visual metaphor to represent many-to-many relationships. This work proposes an approach for visualizing frequent itemsets and many-to-many association rules by a novel use of parallel coordinates. An association rule is visualized by connecting items in the rule, one item on each parallel coordinate, with continuous polynomial curves. In the presence of item taxonomy, each coordinate can be used to visualize an item taxonomy tree which can be expanded or shrunk by user interaction. This user interaction introduces a border, which separates displayable itemsets from nondisplayable ones, in the generalized itemset lattice. Only those itemsets that are both frequent and displayable are considered to be displayed. This approach of visualizing frequent itemsets and association rules has the following features: 1) It is capable of visualizing many-to-many rules and itemsets with many items. 2) It is capable of visualizing a large number of itemsets or rules by displaying only those ones whose items are selected by the user. 3) The closure properties of frequent itemsets and association rules are inherently supported such that the implied ones are not displayed. Usefulness of this approach is demonstrated through examples.
Keywords :
curve fitting; data mining; data visualisation; polynomials; set theory; tree data structures; user interfaces; continuous polynomial curve; data mining algorithm; data visualization; frequent itemset lattice; generalized association rules visualization; interactive data exploration; item taxonomy tree; many-to-many relationships; parallel coordinates; user interaction; visual metaphor; Association rules; Data mining; Humans; Itemsets; Joining processes; Lattices; Polynomials; Taxonomy; Testing; Visualization; 65; Index Terms- Association rules; data mining; data visualization; interactive data exploration; mining methods and algorithms.;
fLanguage :
English
Journal_Title :
Knowledge and Data Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
1041-4347
Type :
jour
DOI :
10.1109/TKDE.2005.14
Filename :
1363765
Link To Document :
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