Lowdimensional Additive Overlapping Clustering

作     者：Depril, Dirk Van Mechelen, Iven Wilderjans, Tom F. 

作者机构：Katholieke Univ Leuven Fac Psychol & Educ Sci B-3000 Louvain Belgium 

出 版 物：《JOURNAL OF CLASSIFICATION》 (分类杂志)

年 卷 期：2012年第29卷第3期

页      面：297-320页

核心收录：

学科分类：0402[教育学-心理学(可授教育学、理学学位）] 07[理学] 08[工学] 0714[理学-统计学（可授理学、经济学学位）] 0701[理学-数学] 

基　　金：Research Fund of KU Leuven (PDM-kort project) [3H100377, GOA 2005/04] Belgian Science Policy [IAP P6/03] Fund of Scientific Research (FWO)-Flanders [G.0546.09] 

主　　题：Additive overlapping clustering Dimensional reduction Alternating least squares algorithm Two-way two-mode data Object by variable data 

摘      要：To reveal the structure underlying two-way two-mode object by variable data, Mirkin (1987) has proposed an additive overlapping clustering model. This model implies an overlapping clustering of the objects and a reconstruction of the data, with the reconstructed variable profile of an object being a summation of the variable profiles of the clusters it belongs to. Grasping the additive (overlapping) clustering structure of object by variable data may, however, be seriously hampered in case the data include a very large number of variables. To deal with this problem, we propose a new model that simultaneously clusters the objects in overlapping clusters and reduces the variable space;as such, the model implies that the cluster profiles and, hence, the reconstructed data profiles are constrained to lie in a lowdimensional space. An alternating least squares (ALS) algorithm to fit the new model to a given data set will be presented, along with a simulation study and an illustrative example that makes use of empirical data.