The mining of data entails converting raw data into useful information that can further analyze and derive critical insights. The raw data you obtain from your source can often be in a cluttered condition that is completely unusable. This data needs to be preprocessed to be analyzed, and the steps for the same are listed below.
Data cleaning is the first step of data preprocessing in data mining. Data obtained directly from a source is generally likely to have certain irrelevant rows, incomplete information, or even rogue empty cells.
These elements cause a lot of issues for any data analyst. For instance, the analyst’s platform might fail to recognize the elements and return an error. When you encounter missing data, you can either ignore the rows of data or attempt to fill in the missing values based on a trend or your own assessment. The former is what is generally done.
But a greater problem may arise when you are faced with ‘noisy’ data. To deal with noisy data, which is so cluttered that it cannot be understood by data analysis platforms or any coding platform, many techniques are utilized.
If your data can be sorted, a prevalent method to reduce its noisiness is the ‘binning’ method. In this, the data is divided into bins of equal size. After this, each bin may be replaced by its mean values or boundary values to conduct further analysis.
Another method is ‘smoothing’ the data by using regression. Regression may be linear or multiple, but the motive is to render the data smooth enough for a trend to be visible. A third approach, another prevalent one, is known as ‘clustering.’
In this data preprocessing method in data mining, surrounding data points are clustered into a single group of data, which is then used for further analysis.
The process of data mining generally requires the data to be in a very particular format or syntax. At the very least, the data must be in such a form that it can be analyzed on a data analysis platform and understood. For this purpose, the transformation step of data mining is utilized. There are a few ways in which data may be transformed.
A popular way is normalization. In this approach, every point of data is subtracted from the highest value of data in that field and then divided by the range of data in that field. This reduces the data from arbitrary numbers to a range between -1 and 1.
Attribute selection may also be carried out, in which the data in its current form is converted into a set of simpler attributes by the data analyst. Data discretization is a lesser-used and rather context-specific technique, in which interval levels replace the raw values of a field to make the understanding of the data easier.
In ‘concept hierarchy generation,’ each data point of a particular attribute is converted to a higher hierarchy level. Read more on data transformation in data mining.
We live in a world in which trillions of bytes and rows of data are generated every day. The amount of data being generated is increasing by the day, and comparatively, the infrastructure for handling data is not improving at the same rate. Hence, handling large amounts of data can often be extremely difficult, even impossible, for systems and servers alike.
Due to these issues, data analysts frequently use data reduction as part of data preprocessing in data mining. This reduces the amount of data through the following techniques and makes it easier to analyze.
In data cube aggregation, an element is known as a ‘data cube’ is generated with a huge amount of data, and then every layer of the cube is used as per requirement. A cube can be stored in one system or server and then be used by others.
In ‘attribute subset selection,’ only the attributes of immediate importance for analysis are selected and stored in a separate, smaller dataset.
Numerosity reduction is very similar to the regression step described above. The number of data points is reduced by generating a trend through regression or some other mathematical method.
In ‘dimensionality reducing,’ encoding is used to reduce the volume of data being handled while retrieving all the data.
It is essential to optimize data mining, considering that data is only going to become more important. These steps of data preprocessing in data mining are bound to be useful for any data analyst.
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