The phrase "a set of partition" is not correct in English.
Did you mean "a set of partitions"? You can use the corrected phrase when referring to multiple divisions or segments of a whole, often in contexts like mathematics, computer science, or organizational structures.
Example: "In our analysis, we will consider a set of partitions to better understand the data distribution."
Alternatives: "a collection of partitions" or "a group of partitions".
We introduce a set of partition Δ n ={x0,⋯1,x,x n } of [a,b], where x i (i=0(1)n) are called nodes of the partition.
Calculating a new putative near-solidus melt composition using a set of partition coefficients for each element in each mineral, the modal mineral assemblage of the peridotite, and the composition of the starting peridotite.
To do so, the potential characteristics of sub-components (solution domain of network design) are divided into a set of partitions and then HFC-APSO is utilized to explore/exploit each of those partitions.
As we described above, the modularity-based algorithms find a set of partitions that with high modularity Q by iteratively selecting and merging partition pairs that maximize Eq. (5).
The modularity Q is formally defined as follows: begin{aligned} Q = sum _{iin C}left{ frac{|E_{ii}|}{2|E|} - left( frac{sum _{jin C}|E_{ij}|}{2|E|} right) ^2 right}, end{aligned} (4 where C and |E| are a set of partitions and the total number of edges included in graph G, respectively, and (E_{ij}) is a number of edges between partition i and j.
By using the ARI, given a set of partitions P, |P| = m, we can compute the likelihood of a partition with respect to the others pi ϵ P as L(P| pi) = Pr(pi|P) = ∏j≠iaji, where aji is the ARI between partition pj and pi, and then select the best partition pb with the maximum likelihood.
This process continues until a set of partitioning criteria are no longer met, resulting in terminal nodes.
Since a hierarchical clustering computes a set of partitions but a clustering consists of only one partition, it is necessary to determine a level that defines the final partition.
A hierarchical clustering method produces a set of partitions on different levels 1,..., k with the following properties: Let the partition of level 1 be just the given set of entities.
