Spectral clustering is often carried out by combining spectral data embedding and -means clustering. However, the aims, dimensionality reduction and clustering, are usually not performed jointly. In this brief, we propose a novel approach to finding an optimal spectral embedding for identifying a partition of the set of objects; it iteratively alternates spectral embedding and clustering. In doing so, we show that our model can learn a low-dimensional representation more suited to clustering. Compared with classical spectral clustering methods, the proposed algorithm is not costly and outperforms not only these methods but also other nonnegative matrix factorization variants.
International audienceIn this paper, we present a new SOM-based bi-clustering approach for continuous data. This approach is called Bi-SOM (for Bi-clustering based on Self-Organizing Map). The main goal of bi-clustering aims to simultaneously group the rows and columns of a given data matrix. In addition, we propose in this work to deal with some issues related to this task: (1) the topological visualization of bi-clusters with respect to their neighborhood relation, (2) the optimization of these bi-clusters in macro-blocks and (3) the dimensionality reduction by eliminating noise blocks, iteratively. Finally, experiments are given over several data sets for validating our approach in comparison with other bi-clustering methods
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