Results 231 to 240 of about 942,452 (277)

Efficient Image Retrieval Using Hierarchical K-Means Clustering. [PDF]

Sensors (Basel)
Park D, Hwang Y.
europepmc   +1 more source

Investigating the Quality of Mobile Apps for Drug-Drug Interaction Management Using the Mobile App Rating Scale and K-Means Clustering: Systematic Search of App Stores.

JMIR Mhealth Uhealth
Bhattacharya A, Florez-Arango JF.
europepmc   +1 more source

K-Means Clustering of Hyperpolarised ¹³C-MRI Identifies Intratumoral Perfusion/Metabolism Mismatch in Renal Cell Carcinoma as the Best Predictor of the Highest Grade. [PDF]

Cancers (Basel)
Horvat-Menih I, Khan AS, McLean MA, Duarte J, Serrao E, Ursprung S, Kaggie JD, Gill AB, Priest AN, Crispin-Ortuzar M, Warren AY, Welsh SJ, Mitchell TJ, Stewart GD, Gallagher FA.   +14 more
europepmc   +1 more source

K-Means Clustering Reveals Long-Term Thyrotropin Receptor Antibody Patterns in Graves' Disease: Insights from a 10-Year Study with Implications for Graves' Orbitopathy. [PDF]

J Clin Med
Park J, Kim JH, Choi HY, Kim J, Kim SS, Yang SW.   +5 more
europepmc   +1 more source

Research on Using K-Means Clustering to Explore High-Risk Products with Ethylene Oxide Residues and Their Manufacturers in Taiwan. [PDF]

Foods
Wu LY, Liu FM, Lin WC, Qiu JT, Lin HY, Lin KF.   +5 more
europepmc   +1 more source

A method for mining condition-specific co-expressed genes in Camellia sinensis based on k-means clustering. [PDF]

BMC Plant Biol
Zheng X, Lim PK, Mutwil M, Wang Y.
europepmc   +1 more source

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Epidemic K-Means Clustering

2011 IEEE 11th International Conference on Data Mining Workshops, 2011
The K-Means algorithm for cluster analysis is one of the most influential and popular data mining methods. Its straightforward parallel formulation is well suited for distributed memory systems with reliable interconnection networks. However, in large-scale geographically distributed systems the straightforward parallel algorithm can be rendered ...
Giuseppe Di Fatta, Francesco Blasa, Simone Cafiero, Giancarlo Fortino   +3 more
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Subspace K-means clustering

Behavior Research Methods, 2013
To achieve an insightful clustering of multivariate data, we propose subspace K-means. Its central idea is to model the centroids and cluster residuals in reduced spaces, which allows for dealing with a wide range of cluster types and yields rich interpretations of the clusters. We review the existing related clustering methods, including deterministic,
Timmerman, Marieke E., Ceulemans, Eva, De Roover, Kim, Van Leeuwen, Karla   +3 more
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K-means clustering with manifold

2010 Seventh International Conference on Fuzzy Systems and Knowledge Discovery, 2010
K-means clustering is a popular conventional clustering algorithm. As it does not use the structure information of data sets, sometime the clustering result will be dissatisfied. Manifold learning algorithms can reveal the low-dimensional geometry structure of the data sets.
Lai Wei, Weiming Zeng, Hong Wang
openaire   +1 more source