We also introduce the construction of a dual λ-partition as a way of generating λ′-partitions from a given λ-partition.
The approaches of the second main group, the partitioning algorithms, start from a given group configuration describing a partitioning of the feature space and proceed by exchanging data elements between the groups.
We use the definition of Li et al. [ 1] for the pseudo-periodic partition of a region and extend the algorithm that can select the repeated region from a given long sequence and give the pseudo-periodic partition of the region.
Also, we observed the probability of collisions by experimenting with different cardinalities of such partition sets drawn at random from a given integer range.
To speed up the method, one can start from a given partition/cover delivered by another (fast) algorithm or from a priori information.
The efficacy score measures the degree of simplification achieved by a given network partitioning.
Cluster analysis partitions a given dataset into groups based on specified features.
Segmentation involves the partitioning of a given DNA sequence into regions that are maximally distinct from one another based on a chosen compositional measure [11].
In k-fold cross-validation, k random, (approximately) equal-sized, disjoint partitions of the sample data are constructed, and a given model is trained on (k−1) partitions and tested on the excluded partition.
Li et al. [ 1] gave a O(| x|) algorithm to compute an optimal partition of a given repeated region x.
