Lowdimensional Additive Overlapping Clustering
 Dirk Depril,
 Iven Van Mechelen,
 Tom F. Wilderjans
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To reveal the structure underlying twoway twomode 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.
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 Title
 Lowdimensional Additive Overlapping Clustering
 Journal

Journal of Classification
Volume 29, Issue 3 , pp 297320
 Cover Date
 20121001
 DOI
 10.1007/s0035701291125
 Print ISSN
 01764268
 Online ISSN
 14321343
 Publisher
 SpringerVerlag
 Additional Links
 Topics
 Keywords

 Additive overlapping clustering
 Dimensional reduction
 Alternating least squares algorithm
 Twoway twomode data
 Object by variable data
 Industry Sectors
 Authors

 Dirk Depril ^{(1)}
 Iven Van Mechelen ^{(2)}
 Tom F. Wilderjans ^{(2)}
 Author Affiliations

 1. suAzio Consulting, Antwerp, Belgium
 2. Faculty of Psychology and Educational Sciences, KU Leuven, Andreas Vesaliusstraat 2, Box 3762, B3000, Leuven, Belgium