.png){kind=icon}
.png){kind=icon}
Return
A data mining approach to knowledge discovery from multidimensional cube structures
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2012.11.008.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In this research we present a novel methodology for the discovery of cubes of interest in large multidimensional datasets. Unlike previous research in this area, our approach does not rely on the availability of specialized domain knowledge and instead makes use of robust methods of data reduction such as Principal Component Analysis and Multiple Correspondence Analysis to identify a small subset of numeric and nominal variables that are responsible for capturing the greatest degree of variation in the data and are thus used in generating cubes of interest. Hierarchical clustering was integrated with the use of data reduction in order to gain insights into the dynamics of relationships between variables of interests at different levels of data abstraction. The two case studies that were conducted on two real word datasets revealed that the methodology was able to capture regions of interest that were significant from both the application and statistical perspectives. (C) 2012 Elsevier B.V. All rights reserved.
Keywords:
Data cubes
OLAP analysis
Data mining
Ranked paths
Principal Component Analysis
Multiple Correspondence Analysis
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
K
IF:
7.6
Papers:
1.2W
Citations:
4.5W