First, there are many possible data representations (e.g. entries in a database or files), access and security protocols.
Nevertheless, it is clear that CIF is largely compatible with many existing complementary data representations.
It supports not only the set of simulation models, GUIs and data representations, but also many users running simulations at the same time.
In the partitions generation step, many mechanisms can be applied including the using of: (i) different data representations, (ii) different individual clustering methods, (iii) different parameters initialisation for clustering methods and (iv) data resampling.
Digraphs provide good representation for the asymmetric nature existing in many data sets because many times v j ∈ Λ v i, K does not imply v i ∈ Λ v j, K. 2.
A variety of data representations can be derived from these neuroimaging experiments, which present many unique challenges for the data mining community.
Many sequence alignment and protein structure alignment algorithms have been developed assuming linear data representations and as such are not able to detect circular permutations between proteins.
From a data analysis perspective, many data points in both systems–such as body weight should be very similar or the same, only stored in different representations.
Many data sets show this.
This representation is called the ONMF mutation profile (ONMF-MP); it is used for efficient search and characterization of patients' genome data, and provides basic information for many data mining tasks in translational bioinformatics.
