인위적 데이터를 이용한 군집분석 프로그램간의 비교에 대한 연구

  • 김성호 (한양대학교 경영대학 경영학부) ;
  • 백승익 (한양대학교 경영대학 경영학부)
  • Published : 2001.12.01

Abstract

Over the years, cluster analysis has become a popular tool for marketing and segmentation researchers. There are various methods for cluster analysis. Among them, K-means partitioning cluster analysis is the most popular segmentation method. However, because the cluster analysis is very sensitive to the initial configurations of the data set at hand, it becomes an important issue to select an appropriate starting configuration that is comparable with the clustering of the whole data so as to improve the reliability of the clustering results. Many programs for K-mean cluster analysis employ various methods to choose the initial seeds and compute the centroids of clusters. In this paper, we suggest a methodology to evaluate various clustering programs. Furthermore, to explore the usability of the methodology, we evaluate four clustering programs by using the methodology.

인터넷 비즈니스나 전자상거래와 연관되어 고객관계관리(Customer Relationship management :CRM)에 대한 관심이 널리 확산됨으로 해서 군집분석에 대한 관심이 한층 높아졌고, 다양한 군집분석 프로그램이 시장에 소개되어 지고 있다. 그런, 군집분석 프로그램들은 다른 데이터 분석 기법과는 달리 그들의 성능을 측정하기가 매우 힘들다. 본 논문에서는 이미 알려져 있는 군집구조를 지닌 인위적 데이터를 사용하여 다양한 군집분석 프로그램을 평가할 수 있는 하나의 방법론을 제시하고, 그 방법론의 유용성을 보여 주기 위해 현재 많이 사용하고 있는 네 가지의 군집분석 프로그램을 본 논문에서 제시한 방법론을 사용하여 평가하는데 그 주요 목적을 두고 있다. 본 연구에서 두 가지의 반복적 군집분석 프로그램(Convergent Cluster Analysis:CCA, SPSS의 Clementine), 전통적인 단순군집 프로그램(One-Shot Clustering Program: Howard-Harris 프로그램), 그리고 IBM의 데이터 마이닝 기법 중 하나인 데모그래픽 군집분석 프로그램의 성능을 비교한 결과, 군집분석을 위하여 다른 군집분석 방법 보다 좀 더 지능적으로 초기치를 생성한 CCA방법이 가장 우월한 성능을 보여 주었다.

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