Sentence examples for partitioning of dataset from inspiring English sources
IFC-C algorithm is based on a dual optimization method, which yields simultaneous estimates of the parameters of c-classification functions together with fuzzy c partitioning of dataset based on a distance measure.
Based on clonal selection mechanism in immune system, a dynamic local search based immune automatic clustering algorithm (DLSIAC) is proposed to automatically evolve the number of clusters as well as a proper partition of datasets.
We propose an acceleration method for approximating spatially varying Gaussian filters using a set of spatially invariant Gaussian filters each of which is applied to a segment of some non-disjoint partitioning of the dataset.
After that, the evolution of solutions converges when the near optimal partitioning of the dataset is represented by the fittest solution with the respect of the employed clustering validity index.
In contrast to flat clustering methods, which output a partitioning of the dataset, SAHN methods produce a similarity hierarchy, i.e., they present various flat clusterings with different granularities at once.
For brevity we will compare only the first partitioning of this dataset generated by the different algorithms.
We can draw upon information theory and note that the least biased partitioning of any dataset is given by the principle of maximum entropy, i.e., partitioning into equally populated bins or quantiles.
Further partitioning of the dataset into terciles or quantiles did not yield any significant results, presumably due to sample size.
The blue line summarises a post hoc calculation to estimate the p-value using the optimal partitioning of the dataset.
A partitioning of the dataset with separate models for the three codon positions was evaluated in comparison with a uniform model for the complete dataset.
> -wrap-foot> Cometricstrics for comparing the clusterings [Jaccard Index (Jaccard, 1901); Variation of Information metric (Meila, 2007)] assume that a clustering is a partitioning of a dataset into mutually disjoint sets.
