Co-clustering from Tensor Data

摘要：

　　With the exponential growth of collected data in different fields like recommender system(user,items),text mining(document,term),bioinformatics(individual,gene),co-clustering which is a simultaneous clustering of both dimensions of a data matrix,has become a popular technique.Co-clustering aims to obtain homogeneous blocks leading to an easy simultaneous interpretation of row clusters and column clusters.Many approaches exist,in this paper we rely on the latent block model(LBM)which is flexible allowing to model different types of data matrices.We extend its use to the case of a tensor(3D matrix)data in proposing a Tensor LBM(TLBM)allowing different relations between entities.To show the interest of TLBM,we consider continuous and binary datasets.To estimate the parameters,a variational EM algorithm is developed.Its performances are evaluated on synthetic and real datasets to highlight different possible applications.

关键词： Co-clustering Tensor Data science

作者: Rafika Boutalbi Lazhar Labiod Mohamed Nadif

作者单位: LIPADE,University of Paris Descartes,45 rue des Saints Pères,75006 Paris,France;TRINOV,196 rue Saint LIPADE,University of Paris Descartes,45 rue des Saints Pères,75006 Paris,France

会议类型: 国际会议

会议名称: The 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (第23届亚太知识发现和数据挖掘国际会议(PAKDD2019)

会议地点: 澳门

会议语种:英文

页码: 370-383

在线出版日期: 2019-04-14（万方平台首次上网日期，不代表论文的发表时间）