Heterogeneous source consensus learning via decision propagation and negotiation

Nowadays, enormous amounts of data are continuously generated not only in massive scale, but also from different, sometimes conflicting, views. Therefore, it is important to consolidate different concepts for intelligent decision making. For example, to predict the research areas of some people, the best results are usually achieved by combining and consolidating predictions obtained from the publication network, co-authorship network and the textual content of their publications. Multiple supervised and unsupervised hypotheses can be drawn from these information sources, and negotiating their differences and consolidating decisions usually yields a much more accurate model due to the diversity and heterogeneity of these models. In this paper, we address the problem of "consensus learning" among competing hypotheses, which either rely on outside knowledge (supervised learning) or internal structure (unsupervised clustering). We argue that consensus learning is an NP-hard pro...

• Type: Conference
• Year: 2009
• Where: KDD
• Authors: Jing Gao, Wei Fan, Yizhou Sun, Jiawei Han
• Keywords: Consensus Learning, Data Mining, KDD 2009, Unsupervised Clustering, Unsupervised Hypotheses