A K-nearest-neighbourhood algorithm is implemented to estimate similarity.
Figure 6 a, b shows that ASTS could estimate similarity thresholds close to δ optimal values.
Unlike those solutions, ASTS is capable to automatically and online estimate similarity thresholds associated to individual searches, enhancing the effectiveness and the efficiency of CBVIR processes.
In order to construct complex network from original traffic flow, a weighted Froenius norm is adopt to estimate similarity between multivariate time series, and Principal Component Analysis is implemented to determine the weights.
The main contribution of the present work consists in to present and validate a new effective and efficient approach, named automatic setting similarity threshold (ASTS), to automatically estimate similarity thresholds in content-based visual information retrieval (CBVIR) problems.
Several options to estimate similarity based on assignment of CLOGs are available.
To estimate similarity in gene expression profiles of oocytes at immature and after IVM, samples were subjected to hierarchical clustering.
As a means to estimate similarity to genes from other plants, the sequences were assigned to known information by the BLASTX search (e-value <1 e−5) against protein data sets from TAIR, RAP-DB, JGI Poplar and KOGs.
These two areas normally fall on either side of the retinotopic border (Hasson et al., 2002); thus their preference can also be used to estimate similarity in retinotopy border locations between groups.
