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Showing 1 to 6 of 6 for “"Correlation clustering"”.
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Scalable algorithms for correlation clustering on large graphs
Correlation clustering (CC) is a widely-used clustering paradigm, where objects are represented as graph nodes and clustering is performed based on relationships between objects (positive or negative edges between pairs of nodes). The CC objective is to obtain a graph clustering that minimizes the …
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Quadratic maximization under combinatorial constraints and related applications
… algorithm for the NP-hard problem of Bipartite Correlation Clustering (BCC). Real datasets will typically produce covariance matrices that have full rank, rendering our algorithms not applicable. Our approach is to first obtain a low-rank approximation of the input data and subsequently solve …
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A new approach to calculate and forecast dynamic conditional correlation - the use of a multivariate heteroskedastic mixture model
… little is devoted to modelling time varying correlation. In this research, we extend the current literature on correlation modelling by reviewing existing time-series tools, performing empirical analysis and developing two new conditional heteroscedastic models based on mixture techniques. …
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Active duplicate detection with Bayesian nonparametric models
… a reduction to the well-studied problem of correlation clustering. It also present experimental results demonstrating the effectiveness of this method in a variety of data domains.
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Bridging Theory and Practice in Parallel Clustering
… and machine learning. In particular, graph clustering, or community detection, is an important problem in graph processing that addresses tangible problems including fraud and threat detection, recommendation and search system design, and the detection of functional contributions of proteins …
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Computational methods for personalized cancer genomics
In recent years, cancer treatment strategies have moved towards personalized approaches, specifically tailoring cancer treatments on a single-patient basis using molecular profiles from the patients’ tumor genomes. Knowledge of a patient’s molecular profile can be used to 1) identify the disease …