Ranking Using Transition Probabilities Learned from Multi-Attribute Data

In this paper, as a novel approach, we learn Markov chain transition probabilities for ranking of multi-attribute data from the inherent structures in the data itself. The procedure is inspired by consensus clustering and exploits a suitable form of the PageRank algorithm. This is very much in the spirit of the original PageRank utilizing the hyperlink structure to learn such probabilities. As opposed to existing approaches for ranking multi-attribute data, our method is not dependent on tuning of critical user-specified parameters. Experiments show the benefits of the proposed method.