Cluster Analysis Gets Complicated

Segmentation studies using cluster analysis have become commonplace. However, the data may be affected by collinearity, which can have a strong impact and affect the results of the analysis unless addressed. This article investigates what level presents a problem, why it's a problem, and how to get around it. Simulated data allows a clear demonstration of the issue without clouding it with extraneous factors.

 

Collinearity is a natural problem in clustering.

So how can researchers get around it?
 

Cluster analysis is widely used in segmentation studies for several reasons. First of all, it’s easy to use. In addition, there are many variations of the method, most statistical packages have a clustering option, and for the most part it’s a good analytical technique. Further, the non-hierarchical clustering technique k-means is particularly popular because it’s very fast and can handle large data sets. Cluster analysis is a distance-based method because it uses Euclidean distance (or some variant) in multidimensional space to assign objects to clusters to which they are closest. However, collinearity can become a major problem when such distance based measures are used. It poses a serious problem that, unless addressed, can produce distorted results.

Collinearity can be defined simply as a high level of correlation between two variables. (When more than two variables are involved, this would be called as multicollinearity.) How high does the correlation have to be for the term collinearity to be invoked? While rules of thumb are prevalent, there doesn't appear to be any strict standard even in the case of regression-based key driver analysis. It's also not clear if such rules of thumb would be applicable for segmentation analysis.

Collinearity is a problem in key driver analysis because, when two independent variables are highly correlated, it becomes difficult to accurately partial out their individual impact on the dependent variable. This often results in beta coefficients that don't appear to be reasonable. While this makes it easy to observe the effects of collinearity in the data, developing a solution may not be straightforward.

The problem is different in segmentation using cluster analysis because there’s no dependent variable or beta coefficient. A certain number of observations measured on a specified number of variables are used for creating segments. Each observation belongs to one segment, and each segment can be defined in terms of all the variables used in the analysis. From a marketing research perspective, the objective in each case is to identify groups of observations similar to each other on certain characteristics, or basis variables, with the hope this would translate into opportunities. In a sense, all segmentation methods are trying for internal cohesion and external isolation among the segments.

When variables used in clustering are collinear, some variables get a higher weight than others. If two variables are perfectly correlated, they effectively represent the same concept. But that concept is now represented twice in the data and hence gets twice the weight of all the other variables. The final solution is likely to be skewed in the direction of that concept, which could be a problem if it’s not anticipated. In the case of multiple variables and multicollinearity, the analysis is in effect being conducted on some unknown number of concepts that are a subset of the actual number of variables being used in the analysis.

For example, while the intention may have been to conduct a cluster analysis on 20 variables, it may actually be conducted on seven concepts that may be unequally weighted. In this situation, there could be a large gap between the intention of the analyst (clustering 20 variables) and what happens in reality (segments based on seven concepts). This could cause the segmentation analysis to go in an undesirable direction. Thus, even though cluster analysis deals with people, correlations between variables have an effect on the results of the analysis.

 

Can It Be Demonstrated?

Is it possible to demonstrate the effect of collinearity in clustering? Further, is it possible to show at what level collinearity can become a problem in segmentation analysis? The answer to both questions is yes, if we’re willing to make the following assumptions: (1) Regardless of the data used, certain types of segments are more useful than others and (2) The problem of collinearity in clustering can be demonstrated using the minimum requirement of variables (i.e., two).

These assumptions are not as restrictive as they initially seem. Consider the first assumption. Traditionally, studies that seek to understand segmenting methods (in terms of the best method to use, effect of outliers, or scales) tend to use either real data about which a lot is known, or simulated data where segment membership is known.

However, to demonstrate the effect of collinearity, we need to use data where the level of correlation between variables can be controlled. This rules out the real data option. Creating a data set where segments are pre-defined and correlations can be varied is almost impossible because the two are linked. But in using simulated data where correlation can be controlled, the need for knowing segment membership is averted if good segments can be simply defined as ones with clearly varying values on the variables used.

Segments with uniformly high or low mean values on all the variables generally tend to be less useful than those with a mix of values. Since practicality is what defines the goodness of a segmentation solution, this is an acceptable standard to use. Further, segments with uniformly high or low values on all variables are easy to identify without using any segmentation analysis technique. It’s only in the mix of values that a richer understanding of the data emerges. It could be argued that the very reason for using any sort of multivariate segmentation technique is to be able to identify segments with a useful mix of values on different variables.

 

 

Addressing the second assumption, the problem is a lot easier to demonstrate if we restrict the scope of the analysis to the minimum. Using just two variables is enough to demonstrate collinearity. Since bivariate correlation is usually the issue when conducting analysis, the results are translatable to any number of variables used in an actual analysis when taken two at a time. With only two variables being used, four segments can adequately represent the practically interesting combinations of two variables. Hence the results reported here are only in the two-to four-segment range, although I extended the analysis up to seven segments to see if the pattern of results held.

 

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This article was written by Rajan Sambandam of TRC, a full-service market research provider located in Fort Washington, PA.