On "one of the few" objects

You Wu, Pankaj K. Agarwal, Chengkai Li, Jun Yang, Cong Yu

Abstract: Objects with multiple numeric attributes can be compared within any "subspace" (subset of attributes). In applications such as computational journalism, users are interested in claims of the form: Karl Malone is one of the only two players in NBA history with at least 25,000 points, 12,000 rebounds, and 5,000 assists in one's career . One challenge in identifying such "one-of-the- k " claims ( k = 2 above) is ensuring their "interestingness". A small k is not a good indicator for interestingness, as one can often make such claims for many objects by increasing the dimensionality of the subspace considered. We propose a uniqueness-based interestingness measure for one-of-the-few claims that is intuitive for non-technical users, and we design algorithms for finding all interesting claims (across all subspaces) from a dataset. Sometimes, users are interested primarily in the objects appearing in these claims. Building on our notion of interesting claims, we propose a scheme for ranking objects and an algorithm for computing the top-ranked objects. Using real-world datasets, we evaluate the efficiency of our algorithms as well as the advantage of our object-ranking scheme over popular methods such as Kemeny optimal rank aggregation and weighted-sum ranking.