Principal Component Analysis in Business Intelligence Applications

被引：0
|
作者：
Sevcenco, Ana-Maria [1 ]
Li, Kin Fun [1 ]
机构：
[1] Univ Victoria, Dept Elect & Comp Engn, Victoria, BC, Canada
来源：
2013 EIGHTH INTERNATIONAL CONFERENCE ON P2P, PARALLEL, GRID, CLOUD AND INTERNET COMPUTING (3PGCIC 2013) | 2013年
关键词：
principal component analysis; business analytics; business intelligence; information retreival; consumer profiling; market trends; page rank; dimensionality reduction; recommender;
D O I：
10.1109/3PGCIC.2013.68
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
With the enormous amount of information available on the web, many innovative applications in the domain of business intelligence have emerged. Information regarding market trends, consumer profile, competitors, etc., enables a firm to chart its direction and formulate strategies. However, in this big data era, getting the right information is not an easy task. In this work, we introduce business intelligence (BI) applications in general, and examine the use of principal component analysis (PCA) in these applications. A study case reveals how PCA can be used for identification of relevant keywords as prominent features, as well as reducing the search space for an individual's specific requests, in the context of a BI recommender.
引用
收藏
页码：399 / 404
页数：6
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