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    Please use this identifier to cite or link to this item: http://asiair.asia.edu.tw/ir/handle/310904400/2090

    Title: Capturing and Evaluating Segments: Using Self-Organizing Maps and K-Means in Market Segmentation
    Contributors: Eric Sprott School of Business, Carleton University
    Keywords: market segmentation;cluster analysis;data miningneural networks;self-organizing maps
    Date: 2006-04
    Issue Date: 2009-10-13 15:22:04 (UTC+8)
    Publisher: Asia University
    Abstract: Market segmentation is a vital part of an organization’s marketing because it provides the fundamental framework necessary for effective marketing efforts. In recent years, due to their high performance in engineering, artificial neural networks have also been applied in management research.
    Self-organizing maps, a technique of unsupervised neural networks, are often used for clustering or
    dimensional reduction. This study employs a modified two-stage approach (SOMs and K-means) to
    group customers, compares the performance between the tandem approach and direct K-means clustering, and tests for the existence of clusters and segments. The test results show that a media promotion variable would be a basis for segmentation. Based on the segmenting results, a marketing communication strategy is presented to cope with customers’ expectations.
    Relation: Asian Journal of Management and Humanity Sciences 1(1):1-15
    Appears in Collections:[Asian Journal of Management and Humanity Sciences] v.1 n.1

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