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KM-HMR uses the Euclidean distance as a similarity measure, and in our proposed KM-I2C we use inter and intra-clustering measures to obtain efficient and high-quality clusters with high levels of intra-cluster similarity and low levels of inter-cluster similarity to obtain valuable insights from large datasets.
The main objective of this work was to accelerate and scaleup large datasets to obtain efficient high-quality clusters.
A good clustering algorithm must produce high-quality clusters with high levels of intra-cluster similarity and with low levels of inter-cluster similarity.
By resorting to the MRF approach and the shared κ-nearest neighbor technique, our proposed algorithm is able to find high-quality clusters.
If ω becomes larger and p is decreased, the post-processing removes more clusters, so the remaining high-quality clusters can precisely match functional modules, but more functional modules could not be identified.
However, since the average size of gold standard are roughly equal in different networks and high-quality clusters are rarer in a sparser network, a functional modules identification algorithm should have lower coverage and produce less and roughly equal-size clusters on a sparser network.
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Several hundred rice plants with parent/F1 relationships were used to generate a high-quality cluster file for accurate SNP calling.
Specifically, the most representative points called "exemplars" and the preferences in the source domain are considered for helping in the construction of the high-quality clustering model for insufficient target data.
p. 9 15, 1998; O'Callaghan et al. in Streaming-data algorithms for high-quality clustering.
Among them, it is the key of clustering algorithm to select a cluster head, and research about how to lower the node energy consumption by selecting the cluster head and then form a high-quality cluster has an important significance.
The collection of high-quality clustered data for research may thus be a useful side effect of geographical containment.
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