Sections 4 addresses the detailed steps of the off-line affinity propagation, on-line cluster matching-based coarse positioning, and probability distribution-aided fine positioning, respectively.
In clustering-aided ACO (CACO), fuzzy system structure is learned through on-line clustering.
It uses on-line clustering of the input output data with a recursively calculated spatial proximity measure.
In this paper we define a model, based on log line clustering and Markov chain simulation to create this synthetic log data.
To verify that the TAS-based cell line cluster solutions were not obtained by chance, for instance, owing to the specific data sample only or a single run of the clustering algorithm, we repeated the cell line clustering on 10,000 bootstrap samples of the original 107 cell lines.
Unlike most evolutionary fuzzy systems, where the structure of the fuzzy system is assigned in advance, an on-line fuzzy clustering approach is proposed for system structure learning.
Whereas a large number of dedicated techniques have been recently proposed for static graphs, the design of on-line graph clustering methods tailored for evolving networks is a challenging problem, and much less documented in the literature.
Obviously, this algorithm contains two phases: (1) in the off-line phase, we construct the radio map and conduct the affinity propagation clustering; and (2) in the on-line phase, the cluster matching-based coarse and probability distribution-aided fine positioning will be performed, respectively.
From their observations the authors conclude that cell line groupings based on miRNA expression are generally consistent with tissue type and with cell line clustering based on mRNA expression, although mRNA expression seems to be more informative.
In contrast, the cell line clustering based on the target addiction profiles was effectively independent of the tissue type (Fig. 2B; supplementary material Fig. S1).
As expected, the cancer cell line clustering based on the gene expression profiles was largely driven by the tissue origin of these cells (Fig. 2A,C; permutationermutestof thet of the Kendall coefficient).
