Table:
Advantages	Disadvantages
Hierarchical CA	• Offers a simple yet comprehensive portrayal of clustering solutions• Measures of similarity allow this analysis to be applied to almost any type of research question• Generates an entire set of clustering solutions expediently	• Susceptible to impact of outliers in the data• Not amenable to analyzing large samples
K-means CA	• Results less susceptible to outliers in the data, influence of chosen distance measure, or the inclusion of inappropriate or irrelevant variables• Can analyze extremely large data sets	• Different solutions for each set of seed points and no guarantee of optimal clustering of observations• Not efficient when a large number of potential cluster solutions are to be considered

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|  | Advantages | Disadvantages | 
 | --- | --- | --- | 
 | Hierarchical CA | • Offers a simple yet comprehensive portrayal of clustering solutions• Measures of similarity allow this analysis to be applied to almost any type of research question• Generates an entire set of clustering solutions expediently | • Susceptible to impact of outliers in the data• Not amenable to analyzing large samples | 
 | K-means CA | • Results less susceptible to outliers in the data, influence of chosen distance measure, or the inclusion of inappropriate or irrelevant variables• Can analyze extremely large data sets | • Different solutions for each set of seed points and no guarantee of optimal clustering of observations• Not efficient when a large number of potential cluster solutions are to be considered |