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Although cluster analysis is the procedure most frequently used to define data-based market segments, it is not without problems. This research addresses one of its major problems: the selection of the "best" subset of variables on which to cluster. If this selection is not made carefully, "noisy" variables that contain little clustering information can cause misleading results. To help isolate potentially noisy variables prior to clustering, the authors discuss a new algorithm, the Heuristic Identification of Noisy Variables (HINoV). They demonstrate its robustness with artificial data. In addition, the authors illustrate the potential of HINoV to yield more managerially useful market segments (clusters) when applied to two real marketing data sets. Implementation of HINoV is straightforward and will help avoid a major problem in using K-means cluster analysis for market segment definition, as well as for other similar types of research.

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k-means clustering, optimal weighting, Rand index, Cross validation,

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