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7 篇文献
 
Opportunity map: a visualization framework for fast identification of actionable knowledge  
Data mining techniques frequently find a large number of patterns or rules, which make it very difficult for a human analyst to interpret the results and to find the truly interesting and actionable r......
International Conference on Information and Knowledge Management  2005
2次引用 0 0
Rule interestingness analysis using OLAP operations  
The problem of interestingness of discovered rules has been investigated by many researchers. The issue is that data mining algorithms often generate too many rules, which make it very hard for the us......
Knowledge Discovery and Data Mining  2006
13次引用 0 0
Project proposal: User-controlled construction of parallel coordinate plots  
Parallel coordinate plots [4] are useful for visualizing multidimensional data. They can also quickly become cluttered and difficult to read, especially when displaying a large number of dimensions. P......
0次引用 0 0
Opportunity map: identifying causes of failure - a deployed data mining system  
In this paper, we report a deployed data mining application system for Motorola. Originally, its intended use was for identifying causes of cellular phone failures, but it has been found to be useful ......
Knowledge Discovery and Data Mining  2006
2次引用 0 0
A Visual Data Mining Framework for Convenient Identification of Useful Knowledge  
Data mining algorithms usually generate a large number of rules, which may not always be useful to human users. In this project, we propose a novel visual data-mining framework, called Opportunity Map......
Int. Conf. on Data Mining  2005
18次引用 0 0
A Visual Data Mining Framework for Convenient Identification  
Data mining algorithms usually generate a large number of rules, which may not always be useful to human users. In this project, we propose a novel visual data-mining framework, called Opportunity Map......
0次引用 0 0
Interactive Exploration of Multivariate Categorical Data: Exploiting Ranking Criteria to Reveal Patterns and Outliers  
Abstract — Analyzing multivariate datasets requires users to understand distributions of single variables and at least the two-way relationships between the variables. Lower-dimension projection techn......
0次引用 0 0

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