Results 91 to 100 of about 25,970,551 (304)
rEMM: Extensible Markov Model for Data Stream Clustering in R [PDF]
Clustering streams of continuously arriving data has become an important application of data mining in recent years and efficient algorithms have been proposed by several researchers. However, clustering alone neglects the fact that data in a data stream
Michael Hahsler, Margaret H. Dunham
core
Remote SensingWithin the last few decades, ALS-enhanced two-phase forest inventory has emerged as viable alternative to standard inventory designs. As a relatively new and compound method, there still remains significant potential for its optimisation.
Marek Lisańczuk +2 more
doaj +1 more source
Leucine‐rich glioma inactivated 1 (LGI1) is a ganglioside‐binding protein
FEBS Letters, EarlyView.Neuronal hyperexcitability associated with a decrease/absence of the extracellular protein LGI1 has been suggested to be primarily due to the downregulation of Kv1 channel expression. The molecular mechanisms underlying this decrease have not yet been elucidated.
Kévin Debreux +7 more
wiley +1 more source
Autonomous clustering using rough set theory [PDF]
, 2008 This paper proposes a clustering technique that minimises the need for subjective human intervention and is based on elements of rough set theory. The proposed algorithm is unified in its approach to clustering and makes use of both local and global ...
Chandra Kambhampati +3 more
core +1 more source
A dynamic quantum clustering approach to brain tumor segmentation
Frontiers in ImagingIntroductionData clustering is a key tool in medical image analysis and classification. Traditional methods such as k-means often require prior knowledge of the number of clusters and may perform poorly in complex, high-dimensional medical datasets. This
Jacksson Sánchez +2 more
doaj +1 more source
Metastatic niche shaped by host factors influences disseminated cancer cell fate
FEBS Letters, EarlyView.Metastasis is shaped not only by cancer cells but also by the environments they encounter. This review explores how factors such as aging, diet, the microbiome, lifestyle, and environmental exposures remodel organ‐specific niches in the lung, liver, bone, and brain, influencing where metastatic cells survive, remain dormant, or grow, and ultimately ...
Gwennan Delyth Ward +2 more
wiley +1 more source
Supervised sampling for clustering large data sets [PDF]
, 2010 The problem of clustering large data sets has attracted a lot of current research. The approaches taken are mainly based either on the more efficient implementation or modification of existing methods or/and on the construction of clusters from a ...
Kosmidis, Ioannis, Karlis, Dimitris
core
Clustering analysis of railway driving missions with niching [PDF]
, 2012 A wide number of applications requires classifying or grouping data into a set of categories or clusters. Most popular clustering techniques to achieve this objective are K-means clustering and hierarchical clustering.
Jaafar, Amine +2 more
core +1 more source
Tree-Based Algorithm for Stable and Efficient Data Clustering
Informatics, 2020 The K-means algorithm is a well-known and widely used clustering algorithm due to its simplicity and convergence properties. However, one of the drawbacks of the algorithm is its instability.
Hasan Aljabbouli +2 more
doaj +1 more source
Smaller is better: nanobodies meet NMR
FEBS Letters, EarlyView.Nanobodies are single‐domain antigen‐binding fragments derived from camelid heavy chain antibodies. Their small size, high stability, and exceptional specificity make nanobodies uniquely useful probes for NMR studies of protein dynamics, transient conformational states, and protein–protein interactions.
Oleg Y. Dmitriev
wiley +1 more source