How do you use “pairwise” plots to evaluate the effectiveness of the clustering?
Pairwise testing is a form of software testing in computer science that combines different methods in software. When using the pairwise testing, every input that is fed in a system is tested for all the possible discrete combinations that can be available from the inputs. When testing the effectiveness of the clustering using the pairwise plots, one has to follow two necessary procedures. One of the methods is the actual labeling according to the species, and the other method is labeling according to the k-means clustering.
The attributes of a data set are “purchase decision (Yes/No), Gender (M/F), income group (<10K, 10 50K, >50K). Can you use K means to cluster this data set?
We cannot use the K means to cluster the data set because the data set involves gender. In these cases, gender is a non-categorical variable. Categorical variables can be expanded as a fixed variable and which the gender variable cannot be extended.
Please review and implement the example “Computing Confidence and Lift” given on page 10 (3b – Association Rule) in “R” language
The association Rule in “R” language defines the discrete sets as linked when they influence the action of another set. The association rules are not a predictive method, and it identifies similarities between different sets and also the relationships. The association rule is useful in identifying confidence in the data set that is being analyzed.