Results 101 to 110 of about 30,970,765 (331)
Balanced K-Means for Clustering [PDF]
, 2014 We present a k-means-based clustering algorithm, which optimizes mean square error, for given cluster sizes. A straightforward application is balanced clustering, where the sizes of each cluster are equal. In k-means assignment phase, the algorithm solves the assignment problem by Hungarian algorithm.
Malinen Mikko, Fränti Pasi
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Soil data clustering by using K-means and fuzzy K-means algorithm
Telfor Journal, 2016 A problem of soil clustering based on the chemical characteristics of soil, and proper visual representation of the obtained results, is analysed in the paper. To that aim, K-means and fuzzy K-means algorithms are adapted for soil data clustering.
E. Hot, V. Popović-Bugarin
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Survivors of pediatric brain tumors (PBTs) can experience long‐term social difficulties, impacting quality of life. Beyond medical and environmental factors, family psychosocial risk may play a role in social outcomes by shaping the caregiving environment and may provide intervention options.
Renske H. Houben +4 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Allogeneic hematopoietic stem cell transplantation (alloHSCT) is an essential therapy for several malignant and nonmalignant diseases, but relapse and graft loss remain the principal threats to its success. Routine monitoring of chimerism and minimal residual disease (MRD) enables early detection of imminent recurrence and guides ...
Carmen Junk +10 more
wiley +1 more source
Pediatric Idiopathic Multicentric Castleman Disease Is Often Severe But Responsive to Siltuximab
Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Idiopathic multicentric Castleman disease (iMCD) is a potentially fatal immunologic disorder marked by widespread lymphadenopathy and inflammation. Siltuximab, an interleukin‐6 (IL‐6) inhibitor, is the only FDA‐approved treatment for adult patients with iMCD.
Bridget Austin +17 more
wiley +1 more source
A Geometric Approach to $k$-means
CoRR, 2022 \kmeans clustering is a fundamental problem in many scientific and engineering domains. The optimization problem associated with \kmeans clustering is nonconvex, for which standard algorithms are only guaranteed to find a local optimum. Leveraging the hidden structure of local solutions, we propose a general algorithmic framework for escaping ...
Jiazhen Hong +3 more
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Kernel Probabilistic K-Means Clustering
Sensors, 2021 Kernel fuzzy c-means (KFCM) is a significantly improved version of fuzzy c-means (FCM) for processing linearly inseparable datasets. However, for fuzzification parameter m=1, the problem of KFCM (kernel fuzzy c-means) cannot be solved by Lagrangian ...
Bowen Liu +4 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Numerous international studies have reported declines in new cancer diagnoses, delayed diagnoses and disruptions in cancer treatment following the implementation of COVID‐19 pandemic public health measures, raising concerns that these effects may ultimately contribute to increased cancer mortality.
Friederike Erdmann +8 more
wiley +1 more source
2022 56th Asilomar Conference on Signals, Systems, and Computers, 2022 arXiv admin note: substantial text overlap with arXiv:2012 ...
Romain Cosentino +5 more
openaire +3 more sources