Sentence examples for clustering of data points from inspiring English sources
"clustering of data points" is a correct and usable phrase in written English
It is commonly used in fields such as data analysis, computer science, and statistics to describe the process of grouping data points with similar characteristics or patterns. Example: The researcher used a clustering algorithm to group the data points into distinct categories based on their similarities.
This statistical test was chosen because of a clustering of data points at zero.
Close clustering of data points for each gene along the X-axis dimension demonstrates low variability of measurements between individual animals.
A correlation between the variables results in the clustering of data points along a line.
We observed that, for the majority of TaqMan assays, the clustering of data points was less distinct when MDA product was used as a template, compared to gDNA.
There was an indication that, within the HG broccoli arm, there was less variability among individuals after the intervention than before because of a greater clustering of data points.
The distributions for percent body fat and BMI were similar, however we observed slightly less clustering of data points for DEXA measures than for BMI, when plotted against the two acute phase proteins.
K-means cluster analysis uses an algorithm that minimizes the within sum of squares to identify clusters of data points.
In order to achieve this goal, we will use a process in which condensed clusters of data points are maintained.
Finally, the least-squares-support-vector-machine (LS-SVM) is used to build a submodel for each cluster of data points, and then all the sub-models are integrated into a model for the whole data set.
This is in agreement with the result on the Day plot (Fig. 10a) that define cluster of data points of limited Mrs/Ms (0.21 0.25) and Bcr/Bc (2.3 2.6), falling in the medial leftward side of the PSD region, and close to the theoretical curve 3 for a mixture of single-domain (SD) and multi-domain (MD) particles derived by Dunlop (2002a, b).
In most cases, it is clear that there is a big cluster of data points on the left side of the y = x partition, meaning that kinases inhibited by the same compound are quite distant according to the sequence-based classification (distance 0.6 – 0.8), but rather close according to our fingerprint enrichment-based classification (distance 0 – 0.4).
