Results 71 to 80 of about 942,452 (277)
Silhouette Analysis for Performance Evaluation in Machine Learning with Applications to Clustering
Entropy, 2021 Grouping the objects based on their similarities is an important common task in machine learning applications. Many clustering methods have been developed, among them k-means based clustering methods have been broadly used and several extensions have ...
Meshal Shutaywi, Nezamoddin N. Kachouie
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Privacy Preserving Multi-Server k-means Computation over Horizontally Partitioned Data
, 2019 The k-means clustering is one of the most popular clustering algorithms in data mining. Recently a lot of research has been concentrated on the algorithm when the dataset is divided into multiple parties or when the dataset is too large to be handled by ...
A Likas +13 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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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
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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
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A hybrid clustering algorithm for data mining
, 2012 Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on
Jain, Ravindra
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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
Hierarchical Trust-Tech-Enhanced K-Means Methods and Their Applications to Power Grids
IEEE Open Access Journal of Power and Energy, 2022 K-means has been widely used in solving a wide range of clustering problems arising in engineering and industrial applications, but it still suffers from several issues. To address these issues, a hierarchical K-means method enhanced by Trust-Tech (H-KTT)
Hsiao-Dong Chiang +3 more
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A robust and sparse K-means clustering algorithm [PDF]
, 2012 In many situations where the interest lies in identifying clusters one might expect that not all available variables carry information about these groups. Furthermore, data quality (e.g.
Kondo, Yumi +2 more
core
*K-means and Cluster Models for Cancer Signatures
, 2017 We present *K-means clustering algorithm and source code by expanding statistical clustering methods applied in https://ssrn.com/abstract=2802753 to quantitative finance. *K-means is statistically deterministic without specifying initial centers, etc. We
Kakushadze, Zura, Yu, Willie
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