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The phrase "a data cluster" is correct and usable in written English.
It can be used in contexts related to data analysis, computer science, or statistics, referring to a group of data points that are similar or related in some way.
Example: "The algorithm identified a data cluster that represents customer purchasing behavior."
Alternatives: "a data group" or "a data set".
Exact(1)
Most solutions distribute keys for data evenly around a data cluster using a hash rank, which while normally efficient puts data from the same time range across large swaths of nodes, making accessing ranges a high-load operation.
Similar(59)
A data clustering method partitions unlabeled data sets into clusters and labels them for various goals such as computer vision and pattern recognition.
A data clustering technique and the relaxation of integer scheduling constraints is evaluated and applied to decrease the model solution time.
The basic idea is to transform a data clustering problem into a community detection one.
The DBSCAN algorithm is a data clustering algorithm proposed by Martin Ester which is widely used in machine learning [25].
This high-dimensional feature space is further mapped to 2D plane, and signal detection is then transformed to a data clustering problem [41].
The natural break classification scheme, also called the Jenks classification method, is a data clustering method designed to determine the best arrangement of values into different classes.
FCM is a data clustering method in which a dataset is grouped into N prespecified clusters, with every data point in the dataset belonging to every cluster to a certain degree [11].
As the multipath signal detection is now equivalent to a data clustering problem given the v1 - v2 feature plane, we may blindly divide the received data into two groups (clusters) and hence realize noncoherent signal detection.
Whether you're building out a big data cluster or a super-scaled storage solution, you'll get it done faster on Ubuntu than any other platform, thanks to the amazing work of our cloud community.
The unique contributions of our approach include (a) a new idea of identifying non-isolated outlier clusters and linking the local spectral property to a global outlier removal process; (b) a modified data clustering scheme with a geometric coherence check.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com