The Correlation Clustering problem has been introduced recently [N. Bansal, A. Blum, S. Chawla, Correlation Clustering, in: Proc. 43rd Symp. Foundations of Computer Science, FOCS, 2002, pp. 238–247] as a model for clustering data when a binary relationship between data points is known. More precisely, for each pair of points we have two scores measuring the similarity and dissimilarity respecti...

Recent researches have shown consensus clustering can enhance the accuracy of human action categorization models by combining multiple clusterings, which can be obtained from various types of local descriptors, such as HOG, HOF and MBH. However, consensus clustering yields final clustering without access to the underlying feature representations of the human action data, which always makes the ...

In the context of big-data analysis, clustering technique holds significant importance for effective categorization and organization extensive datasets. However, pinpointing ideal number clusters handling high-dimensional data can be challenging. To tackle these issues, several strategies have been suggested, such as a consensus ensemble that yields more outcomes compared to individual models. ...

Abstract Background Facing the diversity of omics data and difficulty selecting one result over all those produced by several methods, consensus strategies have potential to reconcile multiple inputs produce robust results. Results Here, we introduce ClustOmics, a generic clustering tool that use in context cancer subtyping. ClustOmics relies on non-relational graph database, which allows for s...