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Furthermore, whereas cluster I was enriched for CUTs, clusters II and III contained more SUTs (Table S2), revealing differences not readily apparent from the class-wide analysis in Figure 1.
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Moreover, the results of the cluster analysis allowed small-sized, clear-cut clusters of citizens to appear, which pay attention to specific pig farming attributes.
When number of clusters is specified as two, Normalized-Cut clusters the first and third subtypes together and the second subtype separately.
Initially, we investigated 193 SNPs in the DNA from Cohort 2. There were clear-cut clusters of significant SNPs in a few chromosomal locations.
We focused on a subset of 7 CUT clusters where every gene in the cluster was detected by MS/MS.
According to the research of Foggia et al. [36], the performance of five graph clustering algorithms: fuzzy c-means MST (minimum spanning tree) clustering [41], Markov clustering [34, 42], iterative conductance cutting algorithm [43], geometric MST clustering [44], and normalized cut clustering [40] are evaluated and compared.
The optimal number of clusters was determined by using the hierarchical cut-clustering rule.
How to reduce the computational complexity of graph cut clustering to process the massive complex data has become a challenging problem.
Based on this empirical study, it is confirmed that the normalized cut clustering provides good performance and appears to be robust across application domains.
To overcome this problem, in this study the normalized cut clustering of Shi and Malik [20] was used.
In comparison to the "CUT cluster" (I), the "SUT clusters" (II and III) had more specific Pab1 binding.
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