Earticle

초록

영어

In the era of intense competition among organizations, retaining a customer is a collaborative process. Business organizations are adopting different strategies to facilitate their customers in verity of ways, so that these customers keep on buying from them. Association Rule Mining (ARM) is one of the strategies that find out correspondence/association among the items sold together by applying basket analysis. The clustering technique is also used for different advantages like; recognizing class of most sold products, classifying customers based on their buying behavior and their power of purchase. Different researchers have provided different algorithms for both ARM and Clustering, and are implemented in different data mining tools. This paper is extended version of [4], we have compared the results of Apriori and K-Mean algorithms against their implementation in Weka and XLMiner. For this comparison we have used the transaction data of Sales Day (a super store). The results are very encouraging and also produced valuable information for sales and business improvements. We have also analyzed the data for hidden knowledge and the results showed some very interesting patterns in user buying behavior and buying timings.

목차

Abstract
 1. Introduction
 2. Data Formulation
 3. Experimental Results
  3.1. Association Rule Mining (ARM)
  3.2. Clustering
  3.3. Discussions
 4. Conclusions and Future Work
 References

키워드

저자

• A. M. Khattak [ Department of Computer Engineering, Kyung Hee University, Korea ]
• A. M. Khan [ Department of Computer Engineering, Kyung Hee University, Korea ]
• Sungyoung Lee [ Department of Computer Engineering, Kyung Hee University, Korea ]
• Young-Koo Lee [ Department of Computer Engineering, Kyung Hee University, Korea ]

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