Researchers have proposed optimized data partitioning (ODP) and stratified data partitioning (SDP) methods to partition of input data into training, validation and test datasets.
The data base provides the necessary definitions about discretization and normalization of universes, fuzzy partition of input and output spaces, membership functions (MFs) definitions.
These newly formed contigs will participate in the next round of merging until the partition of contigs also corresponds to a partition of input reads.
The fuzzy inference component contains a rule base that holds fuzzy rules: a database that defines the MFs used in the fuzzy rules, normalizes the input and output universes of discourse, and performs the fuzzy partitioning of the input and the output spaces; and a reasoning mechanism that performs the inference process upon the rules and given condition to derive a reasonable output.
In this paper, we propose a new method to perform sensitivity analysis through a partitioning of the input variables into these two groupings: regressive variables and model parameters.
To achieve this, it uses an incremental HDFS, performs a stable partitioning of the input data, and adds a contraction phase to divide the reduce task into smaller subtasks that can be re-used.
The antecedent structure of the fuzzy system is identified through uniform partition of its input variable.
In order to make good use of the structural information in rule base to decrease the conservatism of the stability analysis, the standard fuzzy partition of the input variables of fuzzy control systems is well defined.
Assuming we have a complexity formula for the memory resources, it is possible to find a partition of the input size space and the different values of the algorithmic constants.
Within those flaws, it can be mentioned the way the partition of the input data is done, the complex migration process, the algorithm's speed, the existence of some parameters that have to be set in order to obtain good solutions, and the convergence is not guaranteed.
