Browse > Article
http://dx.doi.org/10.5351/KJAS.2007.20.1.023

A Strategy Through Segmentation Using Factor and Cluster Analysis: focusing on corporations having a special status  

Cho, Yong-Jun (National Federation of Fisheries Cooperatives Fisheries Economic Institute)
Kim, Yeong-Hwa (Department of Statistics, Chung-Ang University)
Publication Information
The Korean Journal of Applied Statistics / v.20, no.1, 2007 , pp. 23-38 More about this Journal
Abstract
Corporations adopt a segmentation depends on the existence of target variables, in general. In this paper, for the case of no target variables, a strategy through segmentation is proposed for corporations having a special status based on the management index. In case of segmentation using cluster analysis, however, if one classify according to many variables then he will be in face of difficulties in characterizing. Therefore, after extracting representative factors by factor analysis, a segmentation method through 2 step cluster analysis is employed on the basis of these representative factors. As a result, six segmentation groups are found and the resulting strategy is proposed which strengthens prominent factors and makes up defective factors for each group.
Keywords
2 step cluster; factor analysis; segmentation;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
연도 인용수 순위
1 Zhang, T., Ramakrishnan, R. and Livny, M. (1996). BIRCH: an efficient data clustering method for very large databases, Proceedings of the 1996 ACM SIGMOD International Conference on Management of Data, 103-114, Montreal, Canada
2 Kohonen, T. (1982). Self-organized formation of topologically collect feature maps, Biological Cybernetics, 43, 59-69   DOI
3 Selim, S. Z. and Ismail, M. A. (1984), K-means type algorithms: a generalized convergence theorem and characterization of local optimality, IEEE Transactions on Pattern Analysis and Machine Intelligence, 6, 81-87   DOI   ScienceOn
4 남영우, 성은영 (2001). 인자분석과 군집분석에 의한 세계도시의 유형화, <한국도시지리 학회지>, 4, 1-12
5 박성현, 조신섭, 김성수 (2002). <한글 SPSS>, SPSS 아카데미
6 이종상 (2002). 지역유형구분을 위한 요인점수의 군집분석, <대한국토도시계획학회지>, 37, 191-199
7 임종호 (1993). 석회암 풍화산물에 대한 군집분석과 인자분석, <지리학연구>, 22, 73-90
8 조용준, 허준 (2006). 고객가치모형 별 마케팅전략: 백화점 화장품 고객을 중심으로, Journal of the Korean Data Analysis Society, 8, 335-348
9 허명회, 이용구 (2004). K-평균 군집화의 재현성 평가와 응용, <응용통계연구>, 17, 135-144
10 Berry, M. J. A. and Gordon, L. (1997). Data Mining Techniques, John Wiley & Sons, U.S.A
11 Chiu, T., Fang, D., Chen, J., Wang, Y. and Jeris, C. (2001). A robust and scalable clustering algorithm for mixed type attributes in large database environment, Proceedings of the seventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 263-268, San Francisco, California
12 Hand, D. J. (1981). Discrimination and Classification, John Wiley & Sons, New York
13 최호현, 김선범 (2006). 요인분석과 군집분석을 이용한 용도지역의 특성과 유형분류,<한국도시지리학회지 >, 9, 127-136
14 Kovesi, B., Boucher, J. M. and Saoudi, S. (2001). Stochastic K-means algorithm for vector quantization, Pattern Recognition Letters, 22, 603-610   DOI   ScienceOn