Data smoothing is a process that is used to remove noise from the dataset using some algorithms. It allows for highlighting important features present in the dataset. It helps in predicting the patterns. When collecting data, it can be manipulated to eliminate or reduce any variance or any other noise form.
The concept behind data smoothing is that it will be able to identify simple changes to help predict different trends and patterns. This serves as a help to analysts or traders who need to look at a lot of data which can often be difficult to digest for finding patterns that they wouldn’t see otherwise.
We have seen how the noise is removed from the data using the techniques such as binning, regression, clustering.
- Binning: This method splits the sorted data into the number of bins and smoothens the data values in each bin considering the neighborhood values around it.
- Regression: This method identifies the relation among two dependent attributes so that if we have one attribute, it can be used to predict the other attribute.
- Clustering: This method groups similar data values and form a cluster. The values that lie outside a cluster are known as outliers.