Data reduction methods
Many data scientists use large data size in volume for analysis, which takes a long time, though it is very difficult to analyze the data sometimes. In data analytics applications, if you use a large amount of data, it may produce redundant results. In order to overcome such difficulties, we can use data reduction methods.
Data reduction is the transformation of numerical or alphabetical digital information derived empirically or experimentally into a corrected, ordered, and simplified form. Reduced data size is very small in volume and comparatively original, hence, the storage efficiency will increase and at the same time we can minimize the data handling costs and will minimize the analysis time also.
We can use several types of data reduction methods, which are listed as follows:
Filtering and sampling
Binned algorithm
Dimensionality reduction
Filtering and sampling
In data reduction methods, filtering plays an important role. Filtering explains the process of detecting...
