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This book explains how to perform data de-noising, in large scale,
with a satisfactory level of accuracy. Three main issues are
considered. Firstly, how to eliminate the error propagation from
one stage to next stages while developing a filtered model.
Secondly, how to maintain the positional importance of data whilst
purifying it. Finally, preservation of memory in the data is
crucial to extract smart data from noisy big data. If, after the
application of any form of smoothing or filtering, the memory of
the corresponding data changes heavily, then the final data may
lose some important information. This may lead to wrong or
erroneous conclusions. But, when anticipating any loss of
information due to smoothing or filtering, one cannot avoid the
process of denoising as on the other hand any kind of analysis of
big data in the presence of noise can be misleading. So, the entire
process demands very careful execution with efficient and smart
models in order to effectively deal with it.
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