2.txt

This complete matlab for neural network

发信人: GzLi (笑梨), 信区: DataMining
标  题: DM&KD 6(3) <2>
发信站: 南京大学小百合站 (Thu Jul 18 00:37:28 2002), 站内信件

篇名： Cubegrades&colon; Generalizing Association Rules 
刊名： Data Mining and Knowledge Discovery 
ISSN： 1384-5810 
卷期： 6 卷 3 期 出版日期： 200207  
页码： 从 219 页到 257 页共 39 页 
作者： Imieli&nacute;ski Tomasz   Department of Computer Science, Rutgers
, The State University of N.J., Piscataway, N.J. 08854. [e-mail]
.edu
 
Khachiyan Leonid   Department of Computer Science, Rutgers, The State University
 of N.J., Piscataway, N.J. 08854. [e-mail]
 
Abdulghani Amin   Department of Computer Science, Rutgers, The State University
 of N.J., Piscataway, N.J. 08854. [e-mail]
 
 
文摘： 
Cubegrades are a generalization of association rules which represent how 
a set of measures (aggregates) is affected by modifying a cube through specia
lization
 (rolldown), generalization (rollup) and mutation (which is a change in one
 of the cube&apos;s dimensions). Cubegrades are significantly more expressive
 than association rules in capturing trends and patterns in data because 
they can use other standard aggregate measures, in addition to COUNT. Cubegrades
 are atoms which can support sophisticated “what if” analysis tasks dealing
 with behavior of arbitrary aggregates over different database segments. 
As such, cubegrades can be useful in marketing, sales analysis, and other
 typical data mining applications in business.

In this paper we introduce the concept of cubegrades. We define them and 
give examples of their usage. We then describe in detail an important task
 for computing cubegrades&colon; generation of significant cubes which is
 analogous to generating frequent sets. A novel Grid Based Pruning (GBP) 
method is employed for this purpose. We experimentally demonstrate the practi
cality
 of the method. We conclude with a number of open questions and possible 
extensions of the work.
 

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