Results 81 to 90 of about 940,524 (345)
Socially Fair k-Means Clustering
Conference on Fairness, Accountability and Transparency, 2020 We show that the popular k-means clustering algorithm (Lloyd's heuristic), used for a variety of scientific data, can result in outcomes that are unfavorable to subgroups of data (e.g., demographic groups).
Mehrdad Ghadiri, S. Samadi, S. Vempala
semanticscholar +1 more source
Federated learning is a technique that enables the use of distributed datasets for machine learning purposes without requiring data to be pooled, thereby better preserving privacy and ownership of the data. While supervised FL research has grown substantially over the last years, unsupervised FL methods remain scarce.
Swier Garst, Marcel J. T. Reinders
openaire +2 more sources
FEBS Letters, EarlyView.In this study, we found that human cervical‐derived adipocytes maintain intracellular iron level by regulating the expression of iron transport‐related proteins during adrenergic stimulation. Melanotransferrin is predicted to interact with transferrin receptor 1 based on in silico analysis.
Rahaf Alrifai +9 more
wiley +1 more source
Color image segmentation using a spatial k-means clustering algorithm [PDF]
, 2006 This paper details the implementation of a new adaptive technique for color-texture segmentation that is a generalization of the standard K-Means algorithm. The standard K-Means algorithm produces accurate segmentation results only when applied to images
Ilea, Dana E., Whelan, Paul F.
core
Tau acetylation at K331 has limited impact on tau pathology in vivo
FEBS Letters, EarlyView.We mapped tau post‐translational modifications in humanized MAPT knock‐in mice and in amyloid‐bearing double knock‐in mice. Acetylation within the repeat domain, particularly around K331, showed modest increases under amyloid pathology. To test functional relevance, we generated MAPTK331Q knock‐in mice.
Shoko Hashimoto +3 more
wiley +1 more source
Sparse Multi-View K-Means Clustering
IEEE AccessIn machine learning, k-means clustering is an unsupervised leaning technique to partition the data into k clusters that are homogeneous within the cluster and heterogeneous between clusters.
Miin-Shen Yang, Shazia Parveen
doaj +1 more source
Early Warning of Financial Risk Based on K-Means Clustering Algorithm
Complex, 2021 The early warning of financial risk is to identify and analyze existing financial risk factors, determine the possibility and severity of occurring risks, and provide scientific basis for risk prevention and management.
Zhangyao Zhu, Na Liu
semanticscholar +1 more source
The LINEX Weighted k-Means Clustering
Journal of Statistical Theory and Applications (JSTA)LINEX weighted k-means is a version of weighted k-means clustering, which computes the weights of features in each cluster automatically. Determining which entity is belonged to which cluster depends on the cluster centers.
Narges Ahmadzadehgoli +2 more
doaj +1 more source
Implementasi Data Mining Transaksi Penjualan Menggunakan Algoritma Clustering dengan Metode K-Means
Jurnal Saintekom, 2023 The large number of products sold by the Bill Lights Store resulted in a stockpile of several product items due to the large supply of products that were less attractive to customers, resulting in many unsold and under-sold products.
Nur Afiasari +2 more
doaj +1 more source
Structural insights into an engineered feruloyl esterase with improved MHET degrading properties
FEBS Letters, EarlyView.A feruloyl esterase was engineered to mimic key features of MHETase, enhancing the degradation of PET oligomers. Structural and computational analysis reveal how a point mutation stabilizes the active site and reshapes the binding cleft, expading substrate scope.
Panagiota Karampa +5 more
wiley +1 more source