منابع مشابه
Scalable K-Means++
Over half a century old and showing no signs of aging, k-means remains one of the most popular data processing algorithms. As is well-known, a proper initialization of k-means is crucial for obtaining a good final solution. The recently proposed k-means++ initialization algorithm achieves this, obtaining an initial set of centers that is provably close to the optimum solution. A major downside ...
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Although kernel k-means is central for clustering complex data such as images and texts by implicit feature space embedding, its practicality is limited by the quadratic computational complexity. In this paper, we present a novel technique based on scalable centroid approximation that accelerates kernel k-means down to a sub-quadratic complexity. We prove near-optimality of our algorithm, and e...
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Kernel-based clustering algorithms have the ability to capture the non-linear structure in real world data. Among various kernel-based clustering algorithms, kernel k -means has gained popularity due to its simple iterative nature and ease of implementation. However, its run-time complexity and memory footprint increase quadratically in terms of the size of the data set, and hence, large data s...
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We present an Outlier Removal Clustering (ORC) algorithm that provides outlier detection and data clustering simultaneously. The method employs both clustering and outlier discovery to improve estimation of the centroids of the generative distribution. The proposed algorithm consists of two stages. The first stage consist of purely K-means process, while the second stage iteratively removes the...
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ژورنال
عنوان ژورنال: Algorithms
سال: 2020
ISSN: 1999-4893
DOI: 10.3390/a14010006