User-class based service acceptance policy using cluster analysis

Hea Sook Park; W. Yan-Ha; Soon Mi Lee; Young Whan Park; Doo Kwon Baik

doi:10.1007/978-3-540-30555-2_27

User-class based service acceptance policy using cluster analysis

Hea Sook Park, W. Yan-Ha, Soon Mi Lee, Young Whan Park, Doo Kwon Baik

* Honorary professors

Research output: Contribution to journal › Article › peer-review

1 Citation (Scopus)

Abstract

This paper suggests a new policy for consolidating a company's profits by segregating the clients using the contents service and allocating the media server's resources distinctively by clusters using the cluster analysis method of CRM, which is mainly applied to marketing. For the realization of a new service policy, this paper analyzes the level of contribution vis-à-vis the clients' service and profits through the cluster analysis of clients' data applying the K-Means Method. Clients were grouped into 4 clusters according to the contribution level in terms of profits. In addition, to evaluate the efficiency of CRFS within the Client/Server environment, the acceptance rate per class was determined. The results of the experiment showed that the application of CRFS led to the growth of the acceptance rate of clients belonging to the cluster as well as the significant increase in the profits of the company.

Original language	English
Pages (from-to)	237-242
Number of pages	6
Journal	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3347
DOIs	https://doi.org/10.1007/978-3-540-30555-2_27
Publication status	Published - 2004

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

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10.1007/978-3-540-30555-2_27

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title = "User-class based service acceptance policy using cluster analysis",

abstract = "This paper suggests a new policy for consolidating a company's profits by segregating the clients using the contents service and allocating the media server's resources distinctively by clusters using the cluster analysis method of CRM, which is mainly applied to marketing. For the realization of a new service policy, this paper analyzes the level of contribution vis-{\`a}-vis the clients' service and profits through the cluster analysis of clients' data applying the K-Means Method. Clients were grouped into 4 clusters according to the contribution level in terms of profits. In addition, to evaluate the efficiency of CRFS within the Client/Server environment, the acceptance rate per class was determined. The results of the experiment showed that the application of CRFS led to the growth of the acceptance rate of clients belonging to the cluster as well as the significant increase in the profits of the company.",

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T1 - User-class based service acceptance policy using cluster analysis

AU - Park, Hea Sook

AU - Yan-Ha, W.

AU - Lee, Soon Mi

AU - Park, Young Whan

AU - Baik, Doo Kwon

PY - 2004

Y1 - 2004

N2 - This paper suggests a new policy for consolidating a company's profits by segregating the clients using the contents service and allocating the media server's resources distinctively by clusters using the cluster analysis method of CRM, which is mainly applied to marketing. For the realization of a new service policy, this paper analyzes the level of contribution vis-à-vis the clients' service and profits through the cluster analysis of clients' data applying the K-Means Method. Clients were grouped into 4 clusters according to the contribution level in terms of profits. In addition, to evaluate the efficiency of CRFS within the Client/Server environment, the acceptance rate per class was determined. The results of the experiment showed that the application of CRFS led to the growth of the acceptance rate of clients belonging to the cluster as well as the significant increase in the profits of the company.

AB - This paper suggests a new policy for consolidating a company's profits by segregating the clients using the contents service and allocating the media server's resources distinctively by clusters using the cluster analysis method of CRM, which is mainly applied to marketing. For the realization of a new service policy, this paper analyzes the level of contribution vis-à-vis the clients' service and profits through the cluster analysis of clients' data applying the K-Means Method. Clients were grouped into 4 clusters according to the contribution level in terms of profits. In addition, to evaluate the efficiency of CRFS within the Client/Server environment, the acceptance rate per class was determined. The results of the experiment showed that the application of CRFS led to the growth of the acceptance rate of clients belonging to the cluster as well as the significant increase in the profits of the company.

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User-class based service acceptance policy using cluster analysis

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