Dispersed and unstructured datasets are substantial parameters to realize an
exact amount of the required space. Depending upon the size and the data
distribution, especially, if the classes are significantly associating, the level
of granularity to agree a precise classification of the datasets exceeds. The
data complexity is one of the major attributes to govern the proper value of
the granularity, as it has a direct impact on the performance. Dataset
classification exhibits the vital step in complex data analytics and designs to
ensure that dataset is prompt to be efficiently scrutinized. Data collections
are always causing missing, noisy and out-of-the-range values. Data analytics
which has not been wisely classified for problems as such can induce
unreliable outcomes. Hence, classifications for complex data sources help
comfort the accuracy of gathered datasets by machine learning algorithms.
Dataset complexity and pre-processing time reflect the effectiveness of
individual algorithm. Once the complexity of datasets is characterized then
comparatively simpler datasets can further investigate with parallelism
approach. Speedup performance is measured by the execution of MOA
simulation. Our proposed classification approach outperforms and improves
granularity level of complex datasets. 

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