Results 121 to 130 of about 15,644,966 (296)

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

Performance of standard K-means, sparse K-means and randomized K-mean clustering algorithm using the SIFT descriptor clustering algorithm using the SIFT descriptor.

, 2015
Performance of standard K-means, sparse K-means and randomized K-mean clustering algorithm using the SIFT descriptor clustering algorithm using the SIFT descriptor.
Junhai Jiang (773105), Shicheng Guo (171219), Momiao Xiong (86978), Nan Lin (26755)   +3 more
core   +1 more source

Comparative assessment of crystallographic and cryo‐EM models in the Protein Data Bank

FEBS Open Bio, EarlyView.
Raw data obtained by X‐ray crystallography or cryo‐EM result in experimental maps, ultimately fitted by atomic models. Although the physical principles are different, the final results can be viewed, compared, and evaluated in the same way. With cryogenic electron microscopy (cryo‐EM) on track to surpass X‐ray crystallography as the preferred method ...
Alexander Wlodawer, Pawel Rubach, Zbigniew Dauter, Wojciech Dec, Dariusz Brzezinski, Marta Kulik, Wladek Minor, Mariusz Jaskolski   +7 more
wiley   +1 more source

Self-Weighted Multi-View k-means Algorithm [PDF]

Jisuanji gongcheng
With 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

Analysis and Improvement for K-Means Algorithm

, 2011
K-Means algorithm is one of the mostly used foundation algorithm in data mining, it base on a greedy clustering algorithm. This paper will introduce this algorithm and analysis. Then prove the correctness of the algorithm.
Li Xiao, Jing Zhong Xiao
core   +1 more source

Structural and biochemical insights into the thermostable esterase Ta0887 from Thermoplasma acidophilum

FEBS Open Bio, EarlyView.
In this study, a novel esterase from the thermoacidophilic archaeon Thermoplasma acidophilum was biochemically and structurally characterized. Our results demonstrate that Ta0887 is a highly thermostable esterase that preferentially hydrolyzes p‐nitrophenyl hexanoate and possesses an α‐helical cap domain that likely contributes to its substrate ...
Alejandro Delgado‐Rey, Miriam Livier Llamas‐García, Gabriela M. Montero‐Morán, Leticia Santos, Samuel Lara‐González   +4 more
wiley   +1 more source

Optimized clustering with modified K-means algorithm

, 2021
huge data is a big challenge. Clustering technique is able to find hidden patterns and to extract useful information from huge data. Among the techniques, the k-means algorithm is the most commonly used technique for determining optimal number of ...
Alibuhtto, Mohamed Cassim
core   +1 more source

Finding Optimal Number of Clusters Using Heuristic Clustering Algorithms

Ibn Al-Haitham Journal for Pure and Applied Sciences
     The 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

K-means clustering algorithm and improvement methods [PDF]

This article introduces the K-means clustering algorithm as well as several improved K-means methods, including: K-means++, Incremental K-means and Kernel K-means, and describes application scenarios for the K- means ...
German, Y. O., Zhang Hengrui
core  

Dactolisib outperforms Rapamycin in potentiating apoptosis induced by XPO1, Bcl‐2 and Mcl‐1 blockade in preclinical glioblastoma models

FEBS Open Bio, EarlyView.
The PAM pathway and its impact on cellular metabolism. Color‐coded key symbols represent Rapamycin (R, orange, targeting mTOR) and Dactolisib (D, green, targeting PI3K/mTOR), indicating their sites of action. Created with BioRenders.com. Glioblastoma resists therapy through interconnected survival programs.
Kai Zhao, Leonie Meyer, Madita Braun, Katharina Otte, Celine Bitter, Maite Pawlik, Hartmann Raifer, Frederik Helmprobst, Axel Pagenstecher, Hans Urban, Michael C. Burger, Michael W. Ronellenfitsch, Joachim P. Steinbach, Jarmila Jedelská, Shamim Bagheri, Behrooz H. Yousefi, Pietro Di Fazio, Markus Luster, Andreas H. Mahnken, Andreas Neubauer, Andreas Burchert, Christopher Nimsky, Jörg W. Bartsch, Tillmann Rusch   +23 more
wiley   +1 more source