| Abstract: |
To reveal the structure underlying object by variable data, Mirkin (1987) has proposed an additive 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. Recently, Depril and Van Mechelen (2005) proposed an alternating least squares (ALS) algorithm to fit this model to a given data set at hand. Grasping the additive 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 ALS algorithm to fit the new model to a given data set will be presented, along with results of a simulation study to evaluate its performance. |
