Machine Learning for Hackers

Chapter 9. MDS: Visually Exploring US Senator Similarity

Clustering Based on Similarity

There are many situations where we might want to know how similar the members of a group of people are to one another. For instance, suppose that we were a brand marketing company that had just completed a research survey on a potential new brand. In the survey, we showed a group of people several features of the brand and asked them to rank the brand on each of these features using a five-point scale. We also collected a bunch of socioeconomic data from the subjects, such as age, gender, race, what zip code they live in, and their approximate annual income.

From this survey, we want to understand how the brand appeals across all of these socioeconomic variables. Most importantly, we want to know whether the brand has broad appeal. An alternative way of thinking about this problem is we want to see whether individuals who like most of the brand features have diverse socioeconomic features. A useful means of doing this would be to visualize how the survey respondents cluster. We could then use various visual cues to indicate their memberships in different socioeconomic categories. That is, we would want to see a large amount of mixing between gender, as well as among races and economic stratification.

Likewise, we could use this knowledge to see how close groups clustered based on the brand’s appeal. We could also see how many people were in one cluster as compared to others, or how far away other ...