Results 31 to 40 of about 559,125 (302)
The algorithm of noisy k-means
CoRR, 2013 In this note, we introduce a new algorithm to deal with finite dimensional clustering with errors in variables. The design of this algorithm is based on recent theoretical advances (see Loustau (2013a,b)) in statistical learning with errors in variables.
Camille Brunet, Sébastien Loustau
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Review of K-means Algorithm Optimization Based on Differential Privacy [PDF]
Jisuanji kexue, 2022 Differential privacy K-means algorithm (DP K-means),as a kind of privacy preserving data mining (PPDM) model based on differential privacy technology,has attracted much attention from researchers because of its simplicity,efficiency and ability to ...
KONG Yu-ting, TAN Fu-xiang, ZHAO Xin, ZHANG Zheng-hang, BAI Lu, QIAN Yu-rong
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Soil data clustering by using K-means and fuzzy K-means algorithm
Telfor Journal, 2016 A problem of soil clustering based on the chemical characteristics of soil, and proper visual representation of the obtained results, is analysed in the paper. To that aim, K-means and fuzzy K-means algorithms are adapted for soil data clustering.
E. Hot, V. Popović-Bugarin
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Parallel Implementation of K-Means Algorithm on FPGA
IEEE Access, 2020 The K-means algorithm is widely used to find correlations between data in different application domains. However, given the massive amount of data stored, known as Big Data, the need for high-speed processing to analyze data has become even more critical,
Leonardo A. Dias +2 more
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Parallelization of the K-Means++ Clustering Algorithm
Ingénierie des systèmes d information, 2021 K-means++ is the clustering algorithm that is created to improve the process of getting initial clusters in the K-means algorithm. The k-means++ algorithm selects initial k-centroids arbitrarily dependent on a probability that is proportional to each data-point distance to the existing centroids.
Sara Daoudi +3 more
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An Algorithm for Online K-Means Clustering [PDF]
2016 Proceedings of the Eighteenth Workshop on Algorithm Engineering and Experiments (ALENEX), 2015 This paper shows that one can be competitive with the k-means objective while operating online. In this model, the algorithm receives vectors v_1,...,v_n one by one in an arbitrary order. For each vector the algorithm outputs a cluster identifier before receiving the next one.
Edo Liberty +2 more
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Patient Data Analysis with the Quantum Clustering Method
Quantum Reports, 2023 Quantum computing is one of the most promising solutions for solving optimization problems in the healthcare world. Quantum computing development aims to light up the execution of a vast and complex set of algorithmic instructions. For its implementation,
Shradha Deshmukh +2 more
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, 2020 Forest and land fires are disasters that often occur in Indonesia. In 2007, 2012 and 2015 forest fires that occurred in Sumatra and Kalimantan attracted global attention because they brought smog pollution to neighboring countries.
Sutoyo, Edi, Khairani, Nabila Amalia
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On the Consistency of k -means++ algorithm [PDF]
Fundamenta Informaticae, 2020 We prove in this paper that the expected value of the objective function of the k-means++ algorithm for samples converges to population expected value. As k-means++, for samples, provides with constant factor approximation for k-means objectives, such an approximation can be achieved for the population with increase of the sample size.
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