Search results for "online clustering"

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A fast and recursive algorithm for clustering large datasets with k-medians

2012

Clustering with fast algorithms large samples of high dimensional data is an important challenge in computational statistics. Borrowing ideas from MacQueen (1967) who introduced a sequential version of the $k$-means algorithm, a new class of recursive stochastic gradient algorithms designed for the $k$-medians loss criterion is proposed. By their recursive nature, these algorithms are very fast and are well adapted to deal with large samples of data that are allowed to arrive sequentially. It is proved that the stochastic gradient algorithm converges almost surely to the set of stationary points of the underlying loss criterion. A particular attention is paid to the averaged versions, which…

Statistics and ProbabilityClustering high-dimensional dataFOS: Computer and information sciencesMathematical optimizationhigh dimensional dataMachine Learning (stat.ML)02 engineering and technologyStochastic approximation01 natural sciencesStatistics - Computation010104 statistics & probabilityk-medoidsStatistics - Machine Learning[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]stochastic approximation0202 electrical engineering electronic engineering information engineeringComputational statisticsrecursive estimatorsAlmost surely[ MATH.MATH-ST ] Mathematics [math]/Statistics [math.ST]0101 mathematicsCluster analysisComputation (stat.CO)Mathematicsaveragingk-medoidsRobbins MonroApplied MathematicsEstimator[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]stochastic gradient[ STAT.TH ] Statistics [stat]/Statistics Theory [stat.TH]MedoidComputational MathematicsComputational Theory and Mathematicsonline clustering020201 artificial intelligence & image processingpartitioning around medoidsAlgorithm
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