Making Interval-Based Clustering Rank-Aware
In online applications, such as online dating, users often query and rank large collections of structured items. Top results tend to be homogeneous, which hinders data exploration. For example, a dating website user who is looking for a partner between 20 and 40 years old, and who sorts the matches by income from higher to lower, will see a large number of matches in their late 30s who hold an MBA degree and work in the financial industry, before seeing any matches in different age groups and walks of life. An alternative to presenting results in a ranked list is to find clusters in the result space, identified by a combination of attributes that correlate with rank.