NDLI: Lowdimensional Additive Overlapping Clustering

Content Provider	Springer Nature Link
Author	Depril, Dirk Mechelen, Iven Wilderjans, Tom F.
Copyright Year	2012
Abstract	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.
Starting Page	297
Ending Page	320
Page Count	24
File Format	PDF
ISSN	01764268
Journal	Journal of Classification
Volume Number	29
Issue Number	3
e-ISSN	14321343
Language	English
Publisher	Springer-Verlag
Publisher Date	2012-08-17
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Additive overlapping clustering Dimensional reduction Alternating least squares algorithm Two-way two-mode data Object by variable data Marketing Bioinformatics Psychometrics Pattern Recognition Signal, Image and Speech Processing Statistical Theory and Methods
Content Type	Text
Resource Type	Article
Subject	Mathematics Psychology Library and Information Sciences Statistics, Probability and Uncertainty

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