Abstract
In the present situation, with the enhancement of virtualization techniques, it
is very essential to keep track of accumulated large-scaled heterogeneous data in every
respect. In addition to that, it is also necessary to prioritize the processing mechanisms
when being linked with clustered data. Sometimes it has been observed that the large
scaled datasets are too complex and therefore, the normal computation mechanisms are
not sufficient or adequate for the specific applications. But it is highly required to
observe the significance of each individual dataset and focus on the responses being
accumulated from other aspects to make a suitable decision and generation of efficient
analytical clustered data. The main aim of such applications is to apply the clustering
gaining merits from evolutionary computation to process the large-scaled data and
based on optimality, the performance of datasets can be measured.
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