Search Results - "Robust clustering algorithm"

A neighborhood-based robust clustering algorithm using Apollonius function kernel by Pourbahrami, Shahin

“…Density Peak Clustering (DPC) is a useful algorithm for grouping data points based on their closeness and density. It is widely used in various areas of…”
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Robust clustering algorithm: The use of soft trimming approach by Taheri, Sona, Bagirov, Adil M., Sultanova, Nargiz, Ordin, Burak

“… The majority of conventional clustering algorithms are sensitive to noise and outliers. Robust clustering algorithms often overcome difficulties associated with noise and outliers and find true cluster structures…”
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A robust clustering algorithm based on the identification of core points and KNN kernel density estimation by Zhou, Zhou, Si, Gangquan, Sun, Haodong, Qu, Kai, Hou, Weicheng

“…Density peaks clustering (DPC) has been proved to be an effective clustering method and applied to many scientific fields. It can detect the density peak…”
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A robust clustering algorithm based on weighted optimization of fuzzy feature difference using shadow sets by Su, Xiao, Yu, Bin, Liu, Yunlong

“… We introduce a novel algorithm—robust clustering algorithm based on weighted optimization of fuzzy feature difference using shadow sets (RWFS…”
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A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements by Li, Ziyue, D'Ambro, Emma L., Schobesberger, Siegfried, Gaston, Cassandra J., Lopez-Hilfiker, Felipe D., Liu, Jiumeng, Shilling, John E., Thornton, Joel A., Cappa, Christopher D.

“…One of the challenges of understanding atmospheric organic aerosol (OA) particles stems from its complex composition. Mass spectrometry is commonly used to…”
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A robust clustering algorithm using spatial fuzzy C-means for brain MR images by Alruwaili, Madallah, Siddiqi, Muhammad Hameed, Javed, Muhammad Arshad

“…Magnetic Resonance Imaging (MRI) is a medical imaging modality that is commonly employed for the analysis of different diseases. However, these images come…”
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A robust clustering algorithm for identifying problematic samples in genome-wide association studies by Bellenguez, Céline, Strange, Amy, Freeman, Colin, Donnelly, Peter, Spencer, Chris C.A.

“…High-throughput genotyping arrays provide an efficient way to survey single nucleotide polymorphisms (SNPs) across the genome in large numbers of individuals…”
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A robust clustering algorithm for analysis of composition-dependent organic aerosol thermal desorption measurements by Li, Ziyue, D'Ambro, Emma L., Schobesberger, Siegfried, Gaston, Cassandra J., Lopez-Hilfiker, Felipe D., Liu, Jiumeng, Shilling, John E., Thornton, Joel A., Cappa, Christopher D.

“…One of the challenges of understanding atmospheric organic aerosol (OA) particles stems from its complex composition. Mass spectrometry is commonly used to…”
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Rock: A robust clustering algorithm for categorical attributes by Guha, Sudipto, Rastogi, Rajeev, Shim, Kyuseok

“…Clustering, in data mining, is useful to discover distribution patterns in the underlying data. Clustering algorithms usually employ a distance metric based…”
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DSET++: A robust clustering algorithm by Hou, Jian, Xu, E, Chi, Lei, Xia, Qi, Qi, Nai-Ming

“…Clustering image pixels is an important technique in image segmentation. While normalized cuts is popularly used in image segmentation, dominant set clustering…”
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MSFKNN-DPC: A Robust Clustering Algorithm for Lung Segmentation in Chest X-Ray Images by Herng, Ooi Wei, Nasir, Aimi Salihah Abdul, Sukor, Abdul Syafiq Abdull

“…Accurate lung region segmentation in chest X-ray (CXR) images is essential for automated diagnosis and disease monitoring, especially in the context of…”
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Policy Evaluation with Nonlinear Trended Outcomes: Covid‐19 Vaccination Rates in the United States by Morgan, Lynn Bergeland, Phillips, Peter C. B., Sul, Donggyu

“…ABSTRACT This paper discusses pitfalls in two way fixed effects (TWFE) regressions when the outcome variables contain nonlinear and possibly stochastic trend…”
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Integration of a Robust Clustering Algorithm and Multiple Sensors to Achieve the Noise Cancellation of Heart Rate by Ni, Jui-Chung, Yang, Chu-Sing, Wang, Yu-Tang

“…Many wearable devices are commonly used to measure vital signs; heart rate is the most frequently monitored physiological information. Due to the weak signal…”
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Robust Clustering Algorithm (RCA): An Elegant Approach to Create Node Clusters without Dead Nodes by Khan, Mohammed Shafakhatullah, Dubey, Shival

“…Within a short period, the growth of wireless devices which relies on corporate networks; the type of bandwidth and the use of connection sensitive…”
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Research on Typical Power Supply Mode of Rural Wind-solar-storage Complementarity based on K-means-Robust Clustering Algorithm by Yang, Jialin, Zhao, Pengxiang, Li, Na, Cong, Lin, He, Xin, Yang, Yue

“… mode and load change, this paper proposes a K-means-Robust clustering algorithm to predict the output of microgrid and the classification of rural…”
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Robust growing neural gas algorithm with application in cluster analysis by Qin, A.K., Suganthan, P.N.

“…We propose a novel robust clustering algorithm within the Growing Neural Gas (GNG) framework, called Robust Growing Neural Gas…”
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A similarity-based robust clustering method by Yang, Miin-Shen, Wu, Kuo-Lung

“…This paper presents an alternating optimization clustering procedure called a similarity-based clustering method (SCM). It is an effective and robust approach…”
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A robust clustering algorithm based on competitive agglomeration and soft rejection of outliers by Frigui, H., Krishnapuram, R.

“…We present a new clustering algorithm that addresses two major issues associated with conventional partitional clustering: the difficulty in determining the…”
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ROCK: a robust clustering algorithm for categorical attributes by Guha, S., Rastogi, R., Shim, K.

“…We study clustering algorithms for data with Boolean and categorical attributes. We show that traditional clustering algorithms that use distances between…”
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Robust clustering algorithm for high dimensional data classification based on multiple supports by Lam, B.S.Y., Hong Yan

“…High dimensionality, noisy features and outliers can cause problems in cluster analysis. Many existing methods can handle one of the problems well but not the…”
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