Communication Dans Un Congrès Année : 2025

Sparse weighted multi-view clustering

Résumé

We address multi-view clustering where observations are described by variables divided in several homogeneous and meaningful blocks. As the blocks are supposed to be homogeneous, preserving this block homogeneity would help to exhibit the underlying structure of the individuals. So at a first level, the individuals are clustered according to each block separately and the resulting partitions (called contributory partitions) are aggregated in a consensual partition in a second step. Therefore, this multi-view clustering issue is reformulated as a consensus of partitions one. The choice of the first step clustering method is not addressed here. We only focus on the aggregation of the obtained partitions. These partitions are seen as categorical variables and then associated to indicator matrices and association matrices whose entries are 1 if two individuals are in the same cluster and 0 if not. Using association matrices avoid the label swtiching issue. It has been pointed out by several authors, that simple consensus methods such as CSPA (Cluster based Similarity Partitioning Algorithm) can yield unstable results when the contributory partitions are significantly different and if some of input partitions are highly correlated. This redundancy could bias the final partition towards these correlated partitions. We propose a sparse weighted consensus method based on the RV correlation coefficient between the association matrices to find an unique partition from contributory partitions. The results on simulated data as well as real ones show the relevance of the proposed method particulary when dealing with redundant partitions.

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Dates et versions

hal-05146046 , version 1 (05-07-2025)

Identifiants

  • HAL Id : hal-05146046 , version 1

Citer

Ndèye Niang, Mory Ouattara, Mouhamadou Lamine Ndao. Sparse weighted multi-view clustering. 9th conference of Data Science, Statistics, and Visualisation (DSSV 2025), Jul 2025, Kruger National Park, South Africa. ⟨hal-05146046⟩
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