Results 121 to 130 of about 559,125 (302)
Finding Optimal Number of Clusters Using Heuristic Clustering Algorithms
Ibn Al-Haitham Journal for Pure and Applied SciencesThe problem of estimating the number of clusters k is considered one of the major challenges for partition clustering. The k-means algorithm is a division-based clustering method where only objects are entered into a set of K, and the algorithm ...
Hanin Haqi, Tareef Kamil Mustafa
doaj +1 more source
FINGER KNUCKLE PRINT RECOGNITION WITH SIFT AND K-MEANS ALGORITHM [PDF]
ICTACT Journal on Image and Video Processing, 2013 In general, the identification and verification are done by passwords, pin number, etc., which is easily cracked by others. Biometrics is a powerful and unique tool based on the anatomical and behavioral characteristics of the human beings in order to ...
A. Muthukumar, S. Kannan
doaj
FEBS Open Bio, EarlyView.Activation of the mitochondrial protein OXR1 increases pSyn129 αSynuclein aggregation by lowering ATP levels and altering mitochondrial membrane potential, particularly in response to MSA‐derived fibrils. In contrast, ablation of the ER protein EMC4 enhances autophagic flux and lysosomal clearance, broadly reducing α‐synuclein aggregates.
Sandesh Neupane +11 more
wiley +1 more source
A Feature-Reduction Multi-View k-Means Clustering Algorithm
IEEE Access, 2019 The k-means clustering algorithm is the oldest and most known method in cluster analysis. It has been widely studied with various extensions and applied in a variety of substantive areas.
Miin-Shen Yang, Kristina P. Sinaga
doaj +1 more source
FEBS Open Bio, EarlyView.Evolutionary analysis across 32 placental mammals identified positive selection at residues H148 and W149 in the immune receptor FcγR1. Ancestral reconstruction combined with molecular dynamics simulations reveals how these mutations may influence receptor structure and dynamics, providing insight into the evolution of antibody recognition and immune ...
David A. Young +7 more
wiley +1 more source
Self-Weighted Multi-View k-means Algorithm [PDF]
Jisuanji gongchengWith advancements in information technology, people can use increasingly diversified and complex ways to describe things more accurately, which has led to the emergence of multi-view data. Clustering multi-view data is a fundamental topic in data mining,
LIN Hechuan, XU Huiying, ZHU Xinzhong, HUANG Xiao, LIU Ziyang
doaj +1 more source
Clustering Affine Subspaces: Algorithms and Hardness [PDF]
, 2012 We study a generalization of the famous k-center problem where each object is an affine subspace of dimension Δ, and give either the first or significantly improved algorithms and hardness results for many combinations of parameters.
Lee, Euiwoong
core +1 more source
FEBS Open Bio, EarlyView.The dFoCC pipeline starts with observed DED and resting‐state coordinates, which are then used to generate a library of triggered states. Correlation analysis of the calculated DED features of each candidate vs observed DED permits quantitative evaluation of candidate structural quality.
Meng Iao Fong +3 more
wiley +1 more source
Artificial Bee Colony Algorithm Based on K -Means Clustering for Multiobjective Optimal Power Flow Problem [PDF]
, 2015 An improved multiobjective ABC algorithm based on K -means clustering, called CMOABC, is proposed. To fasten the convergence rate of the canonical MOABC, the way of information communication in the employed bees' phase is modified.
Sun LL(孙丽玲) +2 more
core
Classifying Epileptic EEG Signals with Delay Permutation Entropy and Multi-scale K-Means
, 2014 Most epileptic EEG classification algorithms are supervised and require large training datasets, that hinder their use in real time applications. This chapter proposes an unsupervised Multi-Scale K-means (MSK-means) algorithm to distinguish epileptic EEG
Zhu, Guohun +7 more
core +1 more source