Results 81 to 90 of about 17,769,086 (225)
JOIV: International Journal on Informatics Visualization, 2023 The menu is one of the most fundamental aspects of business continuity in the culinary industry. One of the tools that can be used for menu analysis is menu engineering.
Nina Setiyawati +2 more
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BOOTSTRAPPING K-MEANS CLUSTERING
Journal of the Japanese Society of Computational Statistics, 1990 Summary: Independent observations \(X_ 1,X_ 2,\ldots,X_ n\) are made on a distribution \(F\) on \(R^ d\). To divide these observations into \(k\) clusters, first choose a vector of optimal cluster centers \(b_ n=(b_{n1},b_{n2},\ldots,b_{nk})\) to minimize \(W_ n(a)=n^{- 1}\sum^ n_{i=1}\min_{1\leq j\leq k}\| X_ i-a_ j\|^ 2\) as a function of \(a=(a_ 1 ...
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Ilkom Jurnal Ilmiah, 2021 The coronavirus spreads quickly through human-to-human transmission via close contact and respiratory droplets such as coughing or sneezing. Various studies have been carried out to deal with Covid-19.
Wargijono Utomo
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Towards explaining the speed of $k$-means [PDF]
, 2011 The $k$-means method is a popular algorithm for clustering, known for its speed in practice. This stands in contrast to its exponential worst-case running-time. To explain the speed of the $k$-means method, a smoothed analysis has been conducted.
Manthey, Bodo
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Solving $k$-means on High-dimensional Big Data
, 2015 In recent years, there have been major efforts to develop data stream algorithms that process inputs in one pass over the data with little memory requirement.
AK Jain +12 more
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Profiling Academic Library Patrons using K-means and X-means Clustering
International Journal of Technology, 2019 Information technology is now used very often, especially by individuals born between 1982 and 2002 (the Millennial generation). The academic library, which from its beginnings has been a storehouse for information through collections, is becoming ...
Aisyah Larasati +5 more
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Improved Guarantees for k-means++ and k-means++ Parallel
, 2020 In this paper, we study k-means++ and k-means++ parallel, the two most popular algorithms for the classic k-means clustering problem. We provide novel analyses and show improved approximation and bi-criteria approximation guarantees for k-means++ and k-means++ parallel.
Makarychev, Konstantin +2 more
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Randomized Dimensionality Reduction for k-means Clustering [PDF]
, 2013 We study the topic of dimensionality reduction for $k$-means clustering. Dimensionality reduction encompasses the union of two approaches: \emph{feature selection} and \emph{feature extraction}.
Boutsidis, Christos +3 more
core
, 2023 The k-means clustering algorithm (k-means for short) provides a method offinding structure in input examples. It is also called the Lloyd–Forgy algorithm as it was independently introduced by both Stuart Lloyd and Edward Forgy. k-means, like other algorithms you will study in this part of the book, is an unsupervised learning algorithm and, as such ...
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