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Found 7 papers, 0 papers with code
Constant Approximation for Normalized Modularity and Associations Clustering
We study the problem of graph clustering under a broad class of objectives in which the quality of a cluster is defined based on the ratio between the number of edges in the cluster, and the total weight of vertices in the cluster.
Parallel and Efficient Hierarchical k-Median Clustering
In this paper we introduce a new parallel algorithm for the Euclidean hierarchical $k$-median problem that, when using machines with memory $s$ (for $s\in \Omega(\log^2 (n+\Delta+d))$), outputs a hierarchical clustering such that for every fixed value of $k$ the cost of the solution is at most an $O(\min\{d, \log n\} \log \Delta)$ factor larger in expectation than that of an optimal solution.
Spectral Clustering Oracles in Sublinear Time
The main technical contribution is a sublinear time oracle that provides dot product access to the spectral embedding of $G$ by estimating distributions of short random walks from vertices in $G$.
Data Structures and Algorithms
Fast and Accurate $k$-means++ via Rejection Sampling
$k$-means++ \cite{arthur2007k} is a widely used clustering algorithm that is easy to implement, has nice theoretical guarantees and strong empirical performance.
On Coresets for Logistic Regression
For data sets with bounded $\mu(X)$-complexity, we show that a novel sensitivity sampling scheme produces the first provably sublinear $(1\pm\varepsilon)$-coreset.
Theoretical Analysis of the $k$-Means Algorithm - A Survey
The $k$-means algorithm is one of the most widely used clustering heuristics.
Analysis of Agglomerative Clustering
Assuming that the dimension $d$ is a constant, we show that for any $k$ the solution computed by this algorithm is an $O(\log k)$-approximation to the diameter $k$-clustering problem.
