Earticle

초록

영어

As one of the most popular data mining techniques, clustering is an important way of exploratory data analysis and pattern discovery given the explosive increasing amount of dataset. Applying clustering in customer segmentation is a common method to discover high-value customers. However, traditional clustering methods such as k-means are performing single direction (either row or column) on the data matrix, and thus the clustering results might involve cases that are irrelevant to specific dimensions. Besides, traditional clustering is achieved upon the whole set of attributes or variables, and therefore only capable of discovering global information. Along this line, in order to reduce the dimensions and find out potential local patterns in the data matrix, we proposed a bi-clustering algorithm for customer segmentation. Our experiments using supermarket customer dataset improve the effectiveness and efficiency of proposed bi-clustering algorithm.

목차

Abstract
 1. Introduction
 2. Preliminary
 3. Bi-Clustering Algorithm
 4. Experiment
 5. Conclusion
 References

키워드

저자

• Wang Chengduan [ School of computer engineering, Weifang University, 261061, Weifang, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605