Results 41 to 50 of about 17,769,086 (225)
Differentially Private Compressive K-means [PDF]
ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2019 This work addresses the problem of learning from large collections of data with privacy guarantees. The sketched learning framework proposes to deal with the large scale of datasets by compressing them into a single vector of generalized random moments, from which the learning task is then performed.
Schellekens, Vincent +6 more
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Metode Elbow dalam Optimasi Jumlah Cluster pada K-Means Clustering
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, 2023 K-Means clustering merupakan salah satu strategi yang digunakan dalam analisis data dan machine learning untuk mengelompokkan data menjadi beberapa kelompok (cluster) berdasarkan kemiripan fitur atau atributnya.
Nadia Annisa Maori, Evanita Evanita
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Improved Smoothed Analysis of the k-Means Method [PDF]
, 2008 The k-means method is a widely used clustering algorithm. One of its distinguished features is its speed in practice. Its worst-case running-time, however, is exponential, leaving a gap between practical and theoretical performance.
Manthey, Bodo, Röglin, Heiko
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RSKC: An R Package for a Robust and Sparse K-Means Clustering Algorithm
Journal of Statistical Software, 2016 Witten and Tibshirani (2010) proposed an algorithim to simultaneously find clusters and select clustering variables, called sparse K-means (SK-means).
Yumi Kondo +2 more
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, 2021 Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments, but most studies use GIS-based classification methods to conduct susceptibility zonation.
Zizheng Guo +4 more
semanticscholar +1 more source
A fast version of the k-means classification algorithm for astronomical applications [PDF]
, 2014 Context. K-means is a clustering algorithm that has been used to classify large datasets in astronomical databases. It is an unsupervised method, able to cope very different types of problems. Aims.
Almeida, J. Sánchez +1 more
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IOP Conference Series: Materials Science and Engineering, 2018 Clustering is a data mining technique used to analyse data that has variations and the number of lots. Clustering was process of grouping data into a cluster, so they contained data that is as similar as possible and different from other cluster objects.
M. Syakur +3 more
semanticscholar +1 more source
An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling
Annals of Data Science, 2022 K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlapping clusters in which each point is assigned to a group.
M. Zubair +5 more
semanticscholar +1 more source
On Variants of k-means Clustering [PDF]
, 2015 \textit{Clustering problems} often arise in the fields like data mining, machine learning etc. to group a collection of objects into similar groups with respect to a similarity (or dissimilarity) measure.
Bandyapadhyay, Sayan +1 more
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PYTHON MÜHİTİNDƏ K-MEANS, K-MEANS++ VƏ MİNİ BATCH K-MEANS ALQORİTMLƏRİNİN MÜQAYİSƏLİ ANALİZİ
Problems of Information Technology, 2021 Məqalədə k-means alqortitmi və onun modifikasiyalarının Python mühitində müxtəlif ölçülü verilənlərə tətbiqi məsələlərinə baxılır. Eyni zamanda ənənəvi k-means klasterləşdirmə alqoritmi və onun modifikasıyalarının mövcud vəziyyəti, imkanları, çatışmazlıqları, meydana çıxan problemlər tədqiq edilmiş və onların həlli üçün təkliflər verilmişdir. k-means++
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