Conference article in proceedings
The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019
AbstractWe propose a new variant of the k-median problem, where the objective function models not only the cost of assigning data points to cluster representatives, but also a penalty term for disagreement among the representatives. We motivate this novel problem by applications where we are interested in clustering data while avoiding selecting representatives that are too far from each other. For example, we may want to summarize a set of news sources, but avoid selecting ideologically-extreme articles in order to reduce polarization. To solve the proposed k-median formulation we adopt the local-search algorithm of Arya et al. , We show that the algorithm provides a provable approximation guarantee, which becomes constant under a mild assumption on the minimum number of points for each cluster. We experimentally evaluate our problem formulation and proposed algorithm on datasets inspired by the motivating applications. In particular, we experiment with data extracted from Twitter, the US Congress voting records,and popular news sources. The results show that our objective can lead to choosing less polarized groups of representatives without significant loss in representation fidelity.
| openaire: EC/H2020/654024/EU//SoBigData
Approximation algorithms, Clustering, Committee selection, Data mining, K-median, Polarization
Ordozgoiti , B & Gionis , A 2019 , Reconciliation k-median : Clustering with non-polarized representatives . in The Web Conference 2019 - Proceedings of the World Wide Web Conference, WWW 2019 . ACM , pp. 1387-1397 , The Web Conference , San Francisco , California , United States , 13/05/2019 . https://doi.org/10.1145/3308558.3313475