Dec 27, 2016 · In this study, we propose a technique for quantifying the instability of a clustering solution and for finding robust clusters, termed core ...
Clustering is a widely used unsupervised learning method for finding structure in the data but the resulting clusters are typically presented without any ...
Sep 11, 2024 · In this study, we propose a technique for quantifying the instability of a clustering solution and for finding robust clusters, termed core ...
The basic idea is to find stable clusters in the data by considering co-occurrences of data items in multiple clustering solutions obtained, e.g., by ...
In this study, we propose a technique for quantifying the instability of a clustering solution and for finding robust clusters, termed core clusters, which ...
Feb 1, 2023 · TL;DR: We propose spectral algorithms for aggregating labels from multiple clustering algorithms without knowing the optimal matching between ...
[PDF] Clustering Small Samples with Quality Guarantees: Adaptivity with ... - AAAI
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For clustering, we present a wrapper that adaptively applies a base clustering algorithm to a sample S, using the smallest sample that provides the desired ...
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Consider the problem of clustering n objects. One can apply multiple algorithms to produce N potentially different clustersings of the same objects, ...
In this article, we present a novel approach to clustering finite or infinite dimensional objects observed with different uncertainty levels.
In this paper, we adopt a statistical perspective on local graph clustering, and we analyze the performance of the ℓ1-regularized PageRank method (Fountoulakis ...
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