Data normalization is a fundamental component in data mining to ensure consistency in data records. It entails data transformation or turning the original data into a format that enables efficient data processing. The primary goal of data normalization is to reduce or eliminate redundant data in one or more datasets.

Data duplicity is a severe problem because storing data in relational databases and retaining similar data in several locations becomes difficult. In this article, we will discuss the contribution of Data Normalization Techniques in Data Mining process. 

What is Data Mining?

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Data mining is the process of preparing quality data that can help in gaining insights either through data analysis or building machine learning models. Organizations analyze vast amounts of raw data to extract relevant customer trends and patterns by employing data mining techniques, statistical analysis, and visualization technology to acquire fresh insights.

The ultimate purpose of the data mining process is to extract information from a data collection and turn it into a structure that can be understood and used for further data-driven workflows. Extraction of interesting (non-trivial, implicit, previously unknown, and possibly helpful) patterns or information from a large amount of data is also carried out in the data mining process. Data mining is a fast expanding area focused on helping analysts and decision-makers make sensible use of vast amounts of data.

There are various procedures involved in data mining implementation.

Step 1: Conduct market research 

Before you begin, you must have a thorough awareness of your company’s goals, resources, and existing circumstances in relation to its requirements. This would help develop a rigorous data mining strategy that meets the company’s objectives.

Step 2: Data Validation 

Data must be reviewed and matched as it is gathered from multiple sources to ensure there are no bottlenecks in the data collection process. Quality assurance identifies any underlying irregularities in the data, such as missing data interpolation. 

Step 3: Data Cleaning 

It’s estimated that collecting, cleaning, structuring, and anonymizing data takes 90% of the time before analysis. Since data quality is improved using several techniques, it eliminates the data wrangling job of data scientists so that they can focus on building models. Obtaining quality data after data mining helps data scientists quickly build and optimize models for business growth.

Step 4: Transformation

The activities engaged in this step, with five sub-stages, prepare data for final datasets. It entails:

Noise is eliminated from the data during data smoothing.

  • Data Summary: This procedure employs the aggregation of datasets.
  • Data Generalization: This step replaces low-level data with higher-level conceptualizations to make the data more generic.
  • Data normalization: It is the process of defining data ranges so that data is similar across all records.
  • Data Attribute Construction: Before data mining, datasets must be in the set of attributes.
  • Data Smoothing: Noise or unnecessary outliers are removed from the required data.

Step 5: Model Building

Based on the type of data, you can build either machine learning or deep learning models for classifying and finding in-depth patterns. For improved data pattern discovery, several mathematical models based on various circumstances are incorporated during model building.

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What is Data Normalization?

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Normalization is a method for dissecting tables to remove data redundancy (repetition) and standardize the information for better data workflows. It’s a multi-step procedure for converting data into a tabular format and removing duplicate data from relational tables. 

Normalization Techniques in Data Mining is used for reducing the range of an attribute’s values, such as -1.0 to 1.0. Data normalization is mainly used to reduce redundant data, thereby assisting in reducing the size of data for expediting the processing of information. In most cases, Data Normalization Techniques in Data Mining are implemented in classification models.

Normalization Techniques in Data Mining are helpful since it allows you to obtain the following benefits:

  • Applying Normalization Techniques in Data Mining to a collection of normalized data is much easier.
  • Normalization Techniques in Data Mining applied to a collection of normalized data provide more accurate and effective results.
  • Data extraction from databases becomes much faster once the data has been standardized.
  • On normalized data, more specialized data analysis methods may be used.

Why do you need Normalization Techniques in Data Mining?

When dealing with huge data sets, normalization is usually essential to ensure you do not take data consistency and quality for granted. Since you cannot look for issues and resolve every data record in big data, it is critical to use the Normalization Techniques in Data Mining to transform data and ensure consistency.

When many characteristics exist, but their values vary, building models may result in inaccurate predictions. Consequently, they are normalized to put all qualities on the same scale.

There are several reasons for using Normalization Techniques in Data Mining:

  • The Normalization Techniques in Data Mining are becoming more effective and efficient.
  • The data is translated into a format that everyone can understand; the data can be pulled from databases more quickly, and the data can be analyzed in a specified way.

Some Normalization Techniques in Data Mining are extensively used for data transformation and will be addressed in the next part of this article.

Normalization Techniques in Data Mining

There are several data normalization techniques in data mining, but in this article, we will discuss the top three ways: Z-score normalization, min-max normalization, and decimal scaling normalization. The following Normalization Techniques in Data Mining are listed below:

Z-score Normalization 

The Z-Score value is one of the Normalization Techniques in Data Mining that determines how much a data point deviates from the mean. It calculates the standard deviations that are below or above the mean. It might be anywhere between -3 and +3 standard deviations. Z-score normalization techniques in data mining is beneficial for data analysis that requires comparing a value to a mean (average) value, such as test or survey findings.

A person’s weight, for example, is 80 kilograms. Suppose you want to compare that result to the average weight of a population provided in a large table of data. In that case, you’ll need to use Z-score normalization, especially if the weight is measured in kilos.

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Min Max Normalization

Which is simpler to comprehend: the difference between 500 and 1000000 or between 0.5 and 1? The data becomes more understandable when the minimum and highest values range is less. The min-max normalization method converts a dataset into a scale ranging from 0 to 1.

The original data undergoes a linear modification in this data normalization procedure. The minimum and maximum values from the data are retrieved, and each value is changed using the formula below.

Formula: (v – min A) / (max A – min A) *(new_max A – new_min A) + new_min A


  • A is the attribute data.
  • Min(A) and Max(A) are the minima and maxima absolute values of A.
  • v’ is the new value of every entry in the data.
  • v is the old value of every entry in the data.
  • new_max(A), new_min(A) is the max and min value of the range.

Decimal Scaling Normalization 

In data mining, decimal scaling is another way of normalizing. It works by rounding an integer to the nearest decimal point. It normalizes data by shifting the decimal point of the numbers. We divide each data value by the largest absolute value of the data to normalize the data using this approach. The data value, vi, is normalized to vi’ using the formula below.

Formula: v’ = v / 10^j


  • v’ is the new value after decimal scaling is applied.
  • The attribute’s value is represented by V.
  • The decimal point movement is now defined by integer J.

For example, feature F values range from 850 to 825. Assume that j is three. The greatest value of feature F is 850. To use decimal scaling for normalization, we must divide all variables by 1,000. As a result, 850 is normalized to 0,850, and 825 is changed to 0,825.

The decimal points of the data are transformed according to the absolute value of the maximum in this procedure. As a result, the normalized data’s means will always be between 0 and 1.


In this article, you learned about Data Mining, Data Normalization, and why it is important. You also read about top Normalization Techniques in Data Mining. Data Normalization is a method of organizing data across various connected databases. It allows tables to be transformed to eliminate data duplication and undesired features, including insertion, update, and deletion anomalies.

Normalization techniques in data mining are multi-stage procedures that turn data into tables while removing redundant data from relational databases. It is crucial because if the dataset is large and contains many good characteristics, but it is not normalized, one of the features may have an advantage over the others. This issue is solved via Normalization Techniques in Data Mining.

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Freelance Technical Content Writer, Hevo Data

Kavya is passionate about writing on data science who loves in-depth research on complex topics and loves to simplify complex topics related to data integration and analysis through detailed content.

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