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Copper Doping Enhances the Activity and Selectivity of Atomically Precise Ag44 Nanoclusters for Photocatalytic CO2 Reduction

Advanced Functional Materials, EarlyView.
By a simple anti‐Galvanic reaction, up to six copper atoms could be preferably doped into the Ag2(SR)5 staple motifs and Ag20 dodecahedral shell of an atomically precise Ag44(SR)30 nanocluster. When anatase TiO2 is used as substrate, the (AgCu)44/TiO2 photocatalyst exhibited much improved activity in photocatalytic CO2 reduction compared to Ag44/TiO2 ...
Ye Liu, Xi Zheng, Xiufan Liu, Tengyuan Gao, Nicola Pinna, Yu Wang   +5 more
wiley   +1 more source

Fairness-aware K-means clustering in digital mental health for higher education students: a generalizable framework for equitable clustering. [PDF]

JAMIA Open
Alluri P, Chen Z, Thesen T, Jacobson NC, Marrero WJ.   +4 more
europepmc   +1 more source

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A Modified K-Means Algorithm - Two-Layer K-Means Algorithm

2014 Tenth International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2014
In this paper, a modified K-means algorithm is proposed to categorize a set of data. K-means algorithm is a simple and easy clustering method which can efficiently classify a large number of continuous numerical data of high-dimensions. Moreover, the data in each cluster are similar to one another.
Chen Chung Liu, Shao Wei Chu, Yung Kuan Chan, Shyr Shen Yu   +3 more
openaire   +1 more source

K-means-G*: Accelerating k-means clustering algorithm utilizing primitive geometric concepts

Information Sciences, 2022
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Hassan Ismkhan, Mohammad Izadi
openaire   +2 more sources

Extended K-Means Algorithm

2013 5th International Conference on Intelligent Human-Machine Systems and Cybernetics, 2013
In the conventional K-means algorithm, the input data are automatically grouped into corresponding cluster by minimizing the within-cluster sum of squares. However, the traditional K-means algorithm doesn't do any constraints to the number of elements in each group.
Faliu Yi, Inkyu Moon
openaire   +1 more source

Gravitational K-Means Algorithm

2020
The technologies of the present era produce large amount of spatiotemporal data related to various fields. Clustering is a tool to extract useful information from the large repository of data and helps in data analytics. We propose a Gravitational K-Means clustering algorithm as an extension of K-Means algorithm to exploit the idea of relative ...
Mohd. Yousuf Ansari, Anand Prakash, null Mainuddin   +2 more
openaire   +1 more source

Research on k-means Clustering Algorithm: An Improved k-means Clustering Algorithm

2010 Third International Symposium on Intelligent Information Technology and Security Informatics, 2010
Clustering analysis method is one of the main analytical methods in data mining, the method of clustering algorithm will influence the clustering results directly. This paper discusses the standard k-means clustering algorithm and analyzes the shortcomings of standard k-means algorithm, such as the k-means clustering algorithm has to calculate the ...
Shi Na, Liu Xumin, Guan Yong
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K Means Algorithm

2020
This chapter is concerned with the k means algorithm as the most popular clustering algorithm. This chapter begins with the unsupervised version of the KNN algorithm. With respect to the clustering process, we study the two main versions of the k means algorithm: the crisp k means algorithm and the fuzzy k means algorithm.
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K*-Means: An Effective and Efficient K-Means Clustering Algorithm

2016 IEEE International Conferences on Big Data and Cloud Computing (BDCloud), Social Computing and Networking (SocialCom), Sustainable Computing and Communications (SustainCom) (BDCloud-SocialCom-SustainCom), 2016
K-means is a widely used clustering algorithm in field of data mining across different disciplines in the past fifty years. However, k-means heavily depends on the position of initial centers, and the chosen starting centers randomly may lead to poor quality of clustering.
Jianpeng Qi, Yanwei Yu, Lihong Wang, Jinglei Liu   +3 more
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Capped Robust K-means Algorithm

2017 International Conference on Machine Learning and Cybernetics (ICMLC), 2017
K-means algorithm is a classical algorithm and has been widely used in many applications. However, the traditional K-means algorithm is easily influenced by outliers and it usually obtains an unstable clustering result and poor clustering accuracy. In this paper, aiming at K-means algorithm resistant to outliers, we proposed a Capped Robust K-means ...
Ting Zhang, Fang Yuan, Liu Yang
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