What is Normalization in Data Mining and How to Do It?
Companies are increasingly relying on data to learn more about their customers. Thus, data analysts have a bigger responsibility to explore and analyze large blocks of raw data and glean meaningful customer trends and patterns out of it. This is known as data mining. Data analysts use data mining techniques, advanced statistical analysis, and data visualization technologies to gain new insights.
These can help a business develop effective marketing strategies to improve business performance, scale-up sales, and reduce overhead costs. Although there are tools and algorithms for data mining, it is not a cakewalk, as real-world data is heterogeneous. Thus, there are quite a few challenges when it comes to data mining.
One of the common challenges is that, usually, databases contain attributes of different units, range, and scales. Applying algorithms to such drastically ranging data may not deliver accurate results. This calls for data normalization in data mining.
It is a necessary process required to normalize heterogeneous data. Data can be put into a smaller range, such as 0.0 to 1.0 or -1.0 to 1.0. In simple words, data normalization makes data easier to classify and understand.
Why is Normalization in Data Mining Needed?
Data normalization is mainly needed to minimize or exclude duplicate data. Duplicity in data is a critical issue. This is because it is increasingly problematic to store data in relational databases, keeping identical data in more than one place. Normalization in data mining is a beneficial procedure as it allows achieving certain advantages as mentioned below:
- It is a lot easier to apply data mining algorithms on a set of normalized data.
- The results of data mining algorithms applied to a set of normalized data are more accurate and effective.
- Once the data is normalized, the extraction of data from databases becomes a lot faster.
- More specific data analyzing methods can be applied to normalized data.
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3 Popular Techniques for Data Normalization in Data Mining
There are three popular methods to carry out normalization in data mining. They include:
Min Max Normalization
What is easier to understand – the difference between 200 and 1000000 or the difference between 0.2 and 1. Indeed, when the difference between the minimum and maximum values is less, the data becomes more readable. The min-max normalization functions by converting a range of data into a scale that ranges from 0 to 1.
Min-Max Normalization Formula
To understand the formula, here is an example. Suppose a company wants to decide on a promotion based on the years of work experience of its employees. So, it needs to analyze a database that looks like this:
|Employee Name||Years of Experience|
- The minimum value is 8
- The maximum value is 20
As this formula scales the data between 0 and 1,
- The new min is 0
- The new max is 1
Here, V stands for the respective value of the attribute, i.e., 8, 10, 15, 20
After applying the min-max normalization formula, the following are the V’ values for the attributes:
- For 8 years of experience: v’= 0
- For 10 years of experience: v’ = 0.16
- For 15 years of experience: v’ = 0.58
- For 20 years of experience: v’ = 1
So, the min-max normalization can reduce big numbers to much smaller values. This makes it extremely easy to read the difference between the ranging numbers.
Decimal Scaling Normalization
Decimal scaling is another technique for normalization in data mining. It functions by converting a number to a decimal point.
Decimal Scaling Formula
- V’ is the new value after applying the decimal scaling
- V is the respective value of the attribute
Now, integer J defines the movement of decimal points. So, how to define it? It is equal to the number of digits present in the maximum value in the data table. Here is an example:
Suppose a company wants to compare the salaries of the new joiners. Here are the data values:
Now, look for the maximum value in the data. In this case, it is 25,000. Now count the number of digits in this value. In this case, it is ‘5’. So here ‘j’ is equal to 5, i.e 100,000. This means the V (value of the attribute) needs to be divided by 100,000 here.
After applying the zero decimal scaling formula, here are the new values:
|Name||Salary||Salary after Decimal Scaling|
Thus, decimal scaling can tone down big numbers into easy to understand smaller decimal values. Also, data attributed to different units becomes easy to read and understand once it is converted into smaller decimal values.
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Z-Score value is to understand how far the data point is from the mean. Technically, it measures the standard deviations below or above the mean. It ranges from -3 standard deviation up to +3 standard deviation. Z-score normalization in data mining is useful for those kinds of data analysis wherein there is a need to compare a value with respect to a mean(average) value, such as results from tests or surveys.
For example, a person’s weight is 150 pounds. Now, if there is a need to compare that value with the average weight of a population listed in a vast table of data, Z-score normalization is needed to study such values, especially if someone’s weight is recorded in kilograms.
As data comes from different sources, it is very common to have different attributes in any batch of data. Thus, normalization in data mining is like pre-processing and preparing the data for analysis.
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