Co-clustering of multi-entities sparse relational data in microblogging - Miao YU; Wu YANG; Wei WANG; wei SHENGuo (Tongxin xuebao)
For large-scale sparse relation data of multi-entity in microblogging, an efficient co-clustering algorithm was proposed which processed sparse relation data of multi-entity. In order to take full advantage of multi-relational data when using this algorithm, a robust constraint information embedding algorithm was proposed to construct relation ma-trix, and the performance of relation mining was improved by reducing matrix sparsity. In the sparse constraint block coordinate descent framework, relation matrix concurrently obtained cluster indication matrix of different entities by non-negative matrix tri-factorization. In non-negative matrix factorization, to ensure sparse structure of clustering result, a quick solution was achieved through efficient projection algorithm. Experiments on synthetic and real data sets show that proposed algorithm goes beyond all the baselines on three indicators. The improvement is more significant especially when processing extremely sparse data.