Results 81 to 90 of about 559,125 (302)

An efficient k′-means clustering algorithm

Pattern Recognition Letters, 2008
This paper introduces k'-means algorithm that performs correct clustering without pre-assigning the exact number of clusters. This is achieved by minimizing a suggested cost-function. The cost-function extends the mean-square-error cost-function of k-means. The algorithm consists of two separate steps.
openaire   +2 more sources

Deciphering transcriptional plasticity in pancreatic ductal adenocarcinoma reveals alterations in sensory neuron innervation

Molecular Oncology, EarlyView.
Pancreatic sensory neurons innervating healthy and PDAC tissue were retrogradely labeled and profiled by single‐cell RNA sequencing. Tumor‐associated innervation showed a dominant neurofilament‐positive subtype, altered mitochondrial gene signatures, and reduced non‐peptidergic neurons.
Elena Genova, Michele Montrone, Uday Rangaswamy, Francesco Diversi, Irene Schiavo, Denise Ferrarini, Roberta Di Florio, Irene Longo, Michele Coscia, Nicola Zamboni, Giorgia Demontis, Lisa Veghini, Vincenzo Corbo, Remo Sanges, Paul Heppenstall   +14 more
wiley   +1 more source

A K-means Algorithm Based On Feature Weighting

MATEC Web of Conferences, 2018
Cluster analysis is a statistical analysis technique that divides the research objects into relatively homogeneous groups. The core of cluster analysis is to find useful clusters of objects.
Xu Yan, Fu Xueliang, Li Honghui, Dong Gaifang, Wang Qing   +4 more
doaj   +1 more source

Algorithms for finding $k$ in $k$-means

CoRR, 2020
38 ...
Chiranjib Bhattacharyya, Ravindran Kannan, Amit Kumar 0001   +2 more
openaire   +2 more sources

Heterozygous loss‐of‐function alleles associate the conserved 3′‐5′ exoribonuclease EXOSC10 with hypersensitivity to the anticancer drug 5‐fluorouracil

Molecular Oncology, EarlyView.
EXOSC10, an essential nuclear RNA exosome‐associated 3′‐5′ exoribonuclease, is inhibited by the anticancer drug 5‐fluorouracil (5‐FU), and EXOSC10 depletion increases 5‐FU sensitivity. The colon‐cancer variant EXOSC10S402T, located in a proteolysis motif, is stable and nuclear but nonfunctional in vivo.
Radhika Sain, Yann Le Page, Frédéric Percevault, Emmanuelle Becker, Luc Negroni, Almudena Fernandez, Marta Cantero, Diego Muñoz‐Santos, Lluís Montoliu, Cyrille Garnier, Michael Primig   +10 more
wiley   +1 more source

Pengelompokan Komentar Dataset Sentipol dengan Modified K-Means Clustering

JuTISI (Jurnal Teknik Informatika dan Sistem Informasi), 2020
Clustering is a technique in data mining that groups data sets into similar data clusters. One of the algorithms that is commonly used for clustering is K-Means.
Ruddy Cahyanto, Antonius Rachmat Chrismanto, Danny Sebastian   +2 more
doaj   +1 more source

PageRank and The K-Means Clustering Algorithm

CoRR, 2020
We utilize the PageRank vector to generalize the $k$-means clustering algorithm to directed and undirected graphs. We demonstrate that PageRank and other centrality measures can be used in our setting to robustly compute centrality of nodes in a given graph.
Mustafa Hajij, Eyad Said, Robert Todd
openaire   +2 more sources

Adaptor protein CIN85 potentiates the motility of osteosarcoma cells via the Akt/mTOR and MMP2‐COL3A1 axis

Molecular Oncology, EarlyView.
CIN85 is highly expressed in osteosarcoma, particularly in metastatic lesions. Its overexpression increases cell migration and Matrigel invasion, while silencing CIN85 suppresses these behaviors. Transcriptome analysis shows that CIN85 regulates MMP2, COL3A1, and Akt/mTOR signaling. Targeting these pathways reverses CIN85‐induced motility, highlighting
Iryna Horak, Iva Staniczková Zambo, Matěj Přikryl, Peter Múdry, Danica Zapletalová, Jarmila Navrátilová, Jiří Navrátil, Jan Šmarda, Liudmyla Drobot, Petr Beneš, Lucia Knopfová   +10 more
wiley   +1 more source

An Improved NSGA-III Algorithm Using Genetic K-Means Clustering Algorithm

IEEE Access, 2019
The non-dominated sorting genetic algorithm III (NSGA-III) has recently been proposed to solve many-objective optimization problems (MaOPs). While this algorithm achieves good diversity, its convergence is unsatisfactory.
Qingguo Liu, Xinxue Liu, Jian Wu, Yaxiong Li   +3 more
doaj   +1 more source

Improving The Performance Of The K-means Algorithm

CoRR, 2020
The Incremental K-means (IKM), an improved version of K-means (KM), was introduced to improve the clustering quality of KM significantly. However, the speed of IKM is slower than KM. My thesis proposes two algorithms to speed up IKM while remaining the quality of its clustering result approximately.
openaire   +2 more sources