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As segmentation has been one of the central marketing tasks for decades and customer profitability valuation has seen wide study during the past few years, surprisingly, up to this date, there is a gap in marketing research that await a bridge to link up of these two important and closely related dimensions. In this paper, we introduce a decision support system with the goal of maximizing customer equity by segmentation. The decision support system introduced here is unique in that it accommodates the essence of customer profitability valuation into a segmentation scheme in a sensible and flexible manner, that it suggests the number of segments to be determined by the goal of profit maximization instead of some arbitrary numerical criterion, and that central to its technical core the outlier problem which is pervasive in cluster analysis has been addressed by a modified K-Means algorithm so that clustering can reflect the pattern of the majority of ordinary observations in a data set instead of being influenced by a handful of outliers. It followed by a number of test datasets from a public data source and a conclusion remark was made at the end.