A Feature-Based Approach to Market Segmentation via Overlapping K-Centroids Clustering
A Feature-Based Approach to Market Segmentation via Overlapping K-Centroids Clustering
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DOI：
10.1177/002224379703400306
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发表时间：
1997-08
影响因子：
6.1
通讯作者：
A. Chaturvedi;J. Carroll;P. Green;J. A. Rotondo
中科院分区：
文献类型：
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作者：
A. Chaturvedi;J. Carroll;P. Green;J. A. Rotondo
非层次划分技术广泛应用于许多营销应用中，特别是在消费者的聚类中，而不是品牌。这些技术可能对异常值的存在极其敏感，这可能导致对片段的误解，并且随后推断片段与独立定义的可操作变量的不正确关系。作者提出了一种基于重叠集群概念的市场细分的一般方法（谢泼德和Arabie，1979），其中每种重叠模式都可以被解释为一个不同的分区。K-means和K-median聚类过程都是所提出方法的特殊情况。所建议的过程可以处理相对大的数据集（例如，2000个实体），是很容易编程，因此可以在市场研究中有收益地使用。
Nonhierarchical partitioning techniques are used widely in many marketing applications, particularly in the clustering of consumers, as opposed to brands. These techniques can be extremely sensitive to the presence of outliers, which might result in misinterpretations of the segments, and subsequently to inferring incorrect relationships of segments to independently defined, actionable variables. The authors propose a general approach to market segmentation based on the concept of overlapping clusters (Shepard and Arabie 1979), wherein each pattern of overlap can be interpreted as a distinct partition. Both K-means and K-medians clustering procedures are special cases of the proposed approach. The suggested procedure can handle relatively large data sets (e.g., 2000 entities), is easily programmable, and hence can be gainfully employed in marketing research.