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# Historically, tandem clustering was the only practical solution to combining numeric and categorical data. However, modern cluster analysis methods (e.g., SPSS Two Step Clustering) and latent class methods are able to automatically accommodate combinations of different data types, so this justification for the use of tandem clustering is no longer applicable.
# Historically, tandem clustering was the only practical solution to combining numeric and categorical data. However, modern cluster analysis methods (e.g., SPSS Two Step Clustering) and latent class methods are able to automatically accommodate combinations of different data types, so this justification for the use of tandem clustering is no longer applicable.
# Variable standardization is not, and never has been, a good justification for tandem clustering, as variable standardization often undermines the objectives of segmentation studies (see [[Variable Standardization]]).
# Variable standardization is not, and never has been, a good justification for tandem clustering, as variable standardization often undermines the objectives of segmentation studies (see [[Variable Standardization]]).
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# The issue of whether removing redundancies is an appropriate motivation is more difficult. Dimension reduction involves two qualitatively distinct transformations of the data: (1) the most highly correlated variables are combined into dimensions and thus they are down-weighted
relatively
to less important variables; and (2) the least important variables are excluded from further analysis because the dimensions that they are correlated with are usually determined to be immaterial (e.g., have eigenvalues less than 1). Thus, the tandem clustering has the net effect of causing variables with moderate levels of correlation with other variables to be given relatively greater prominence in the segmentation than would otherwise occur.
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# The issue of whether removing redundancies is an appropriate motivation is more difficult. Dimension reduction involves two qualitatively distinct transformations of the data: (1) the most highly correlated variables are combined into dimensions and thus they are down-weighted
relative
to less important variables; and (2) the least important variables are excluded from further analysis because the dimensions that they are correlated with are usually determined to be immaterial (e.g., have eigenvalues less than 1). Thus, the tandem clustering has the net effect of causing variables with moderate levels of correlation with other variables to be given relatively greater prominence in the segmentation than would otherwise occur.
:: If clustering a single set of variables (e.g., ratings of respondents opinions on different topics), it would appear to be unambiguously dangerous to apply tandem clustering as the way that it deals with the redundancies is at odds with the goal of segmentation. That is, in this situation if there are a set of variables that are highly correlated then it is these variables that should be central to the segmentation. Similarly, the variables that are excluded could contain information that is relevant to the formation of the segments.
Green, P. E. and A. M. Krieger (1995). "Alternative approaches to cluster-based market segmentation." Journal of the Market Research Society 37(3): 231-239.
:: If clustering a single set of variables (e.g., ratings of respondents opinions on different topics), it would appear to be unambiguously dangerous to apply tandem clustering as the way that it deals with the redundancies is at odds with the goal of segmentation. That is, in this situation if there are a set of variables that are highly correlated then it is these variables that should be central to the segmentation. Similarly, the variables that are excluded could contain information that is relevant to the formation of the segments.
Green, P. E. and A. M. Krieger (1995). "Alternative approaches to cluster-based market segmentation." Journal of the Market Research Society 37(3): 231-239.
:: Where the segmentation is being conducted with lots of different types of data (e.g., brand preference, behavior, importance ratings), a failure to address the redundancies can result in uninteresting segmentations, so in this situation tandem clustering is more defensible (particularly if time constraints prevent a more considered method of addressing the problem of redundancies).
:: Where the segmentation is being conducted with lots of different types of data (e.g., brand preference, behavior, importance ratings), a failure to address the redundancies can result in uninteresting segmentations, so in this situation tandem clustering is more defensible (particularly if time constraints prevent a more considered method of addressing the problem of redundancies).
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== An alternative to tandem clustering ==
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A
== See also ==
== See also ==
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* [[Tandem Clustering versus Latent Class Analysis]]
* [[Variable Standardization]]
* [[Variable Standardization]]
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* [[Data Preparation for Cluster-Based Segmentation]]
== References ==
== References ==