Data Transformation Process in Data Mining Simplified 101

on Data Automation, Data Integration, data mining, Data Visualization, Data Warehouse, ETL, ETL Tutorials • April 21st, 2022 • Write for Hevo


Raw data is difficult to track down and comprehend. That is why it must be preprocessed before any information can be extracted from it. Data Transformation is a technique for converting raw data into a format that facilitates Data Mining and retrieval of strategic information. Data Transformation entails data cleaning techniques as well as a data reduction technique to convert the data into the proper format.

Companies are hiring Data Scientists to make sense of their business data, as data science is rated as one of the most exciting fields to work in. To uncover hidden information from company databases, these data professionals use a process known as Data Mining.

However, because the majority of this data is unstructured, it may be difficult to comprehend. It must be converted into a more easily analyzed format. The techies use Data Transformation tools to accomplish this.

In this article, we will learn about the various Data Transformation Process in Data Mining. But first, let us define Data Mining.

Table of Contents

What is Data Mining?

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Data Mining is the process of analyzing the data and finding patterns, correlations, and anomalies in large datasets. Data from Employee Databases, Financial Information, Vendor lists, Client Databases, Network Traffic, Customer Accounts, etc are included in these datasets. Huge datasets can be explored manually or automatically using statistics, Machine Learning (ML), and Artificial Intelligence (AI).

The business goal that will be achieved using the data is determined first in the Data Mining process. The data is then gathered from various sources and loaded into Data Warehouses, which serve as a repository for analytical data. Data is also cleansed – missing data is added and duplicate data is removed. To find patterns in data, sophisticated tools and mathematical models are used.

The outcomes are compared to the business objectives to determine whether they can be used in business operations. The data is deployed within the company based on the comparison. It is then presented in the form of simple graphs or tables.

Key Features of Data Mining

These are the features of Data Mining:

  • Prediction of Probable Outcomes
  • Concentrate on Large Datasets and Databases.
  • Based on Behavior Analysis, Automatic Pattern Predictions are made.
  • Any SQL expression can be used to calculate a feature from other features.

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What are the Various Data Mining Methods?

Data is widely available to many organizations. The data is in both structured and unstructured forms, making it difficult for businesses to manage. Data Mining is a process that assists all organizations in detecting patterns and developing insights based on business needs.

There are numerous methods available to assist any organization in converting raw data into actionable insights for improving company growth as such. The following are some of the most commonly used Data Mining methods:

  • Cleaning of data
  • Categorization
  • Regression  
  • Regression  
  • Keeping track of the available patterns
  • Visualization
  • Prognosis
  • Decision Trees
  • Statistical method
  • Recurring patterns

What is Data Transformation Process in Data Mining?

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Data Transformation is used in Data Mining to combine unstructured and structured data for later analysis. It is also important when transferring data to a new cloud data warehouse. It is easier to analyze and search for patterns when the data is homogeneous and well-structured.

For example, suppose a company acquires another firm and now needs to consolidate all of its business data. The smaller firm could be using a different database than the parent company. Furthermore, the data in these databases may have distinct IDs, keys, and values. All of this must be formatted so that all of the records are comparable and evaluable.

Data Transformation Process

ETL refers to the entire Data Transformation Process (Extract, Load, and Transform). Analysts can convert data to the desired format using the ETL process. The following are the steps in the Data Transformation Process:

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  • Data Exploration: Analysts work in the first stage to understand and identify data in its original format. They will use data profiling tools to accomplish this. This step assists analysts in determining what actions are required to convert data into the desired format.
  • Data Mapping: Analysts perform data mapping during this phase to determine how individual fields are modified, mapped, filtered, joined, and aggregated. Data mapping is critical to many data processes, and a single blunder can lead to incorrect analysis and ripple effects throughout your organization.
  • Data Extraction: Analysts extract data from its original source during this phase. Structured sources such as databases may be used, as may streaming sources such as customer log files from web applications.
  • Generation and Execution of Code: After extracting the data, analysts must write code to complete the transformation. Analysts frequently generate codes with the assistance of Data Transformation platforms or tools.
  • Review: After transforming the data, analysts must double-check it to ensure that everything is properly formatted.
  • Sending: The final step is to send the data to its final destination. A data warehouse or database that can handle both structured and unstructured data could be the goal.

Data Transformation Techniques in Data Mining

According to the definition of Data Transformation, the process takes data from a source and converts it into a destination format that can be used for a variety of purposes.

It occurs during the ETL (Extract, Load, Transform) process, where the data must be recognized and extracted from where it is saved and moved into a single repository. This raw data from the Data Transformation Process in Data Mining must be cleansed and prepared for transformation by addressing issues such as missing values and inconsistencies. The following Data Transformation Techniques in Data Mining are then used:

1) Data Smoothing

This technique is used to remove the noise from a dataset. The distorted and meaningless data within a dataset is referred to as noise. Smoothing algorithms are used to highlight the data’s special features. Following the removal of noise, the process can detect any small changes in the data to detect special patterns.

This method can detect any data modification or trend and can assist in the Data Transformation Process in Data Mining.

2) Data Aggregation

Data Aggregation is the process of gathering data from various sources and storing it in a particular standardized format, so it can be retrieved easily. Data is collected, stored, analyzed, and presented in the form of a report or summary.

This is an important step because the Accuracy and Quantity of Data are critical for proper analysis. Companies gather information about their website visitors. This provides them with information about customer demographics and behavior metrics. This Aggregated Data helps them create personalized messages, offers, and discounts and can assist in the Data Transformation Process in Data Mining.

3) Discretization

These Data Transformation Techniques in Data Mining are used for converting continuous data into a series of data intervals. Small interval labels replace continuous attribute values. This makes it easier to study and analyze the data. If a data mining task handles a continuous attribute, its discrete values can be replaced by constant quality attributes. This increases the task’s efficiency.

Because it converts a large dataset into a set of categorical data, this method is also known as a data reduction mechanism. When dealing with discrete values, discretization employs decision tree-based algorithms to produce short, compact, and accurate results which is beneficial in the Data Transformation Process in Data Mining.

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4) Generalization

Using the concept of the Data Transformation Process in Data Mining hierarchies, low-level data attributes are transformed into high-level data attributes in this process. This conversion from a lower to a higher conceptual level is useful for gaining a better understanding of the data. In a dataset, for example, age data can be represented as (20, 30). It is transformed into a categorical value at a higher conceptual level (young, old).

5) Construction of Attributes

The Attribute Construction method generates new attributes from an existing set of attributes. In a dataset of employee information, for example, the attributes could be employee name, employee ID, and address. These attributes can be used to create another dataset that only contains information about employees who started in 2019.

This Data Transformation Process in the Data Mining method improves mining efficiency and speeds up the creation of new datasets.

6) Normalization

This is one of the most important techniques for the Data Transformation Process in Data Mining, also known as data pre-processing. The data is transformed here so that it falls within a specified range. Data modeling and mining can be difficult when attributes are on different ranges or scales. Normalization aids in the application of data mining algorithms and the extraction of data at a faster rate which could play a huge role in the Data Transformation Process in Data Mining.

Benefits of Data Transformation Process in Data Mining

Every industry today is being transformed by the data they can collect on their customers’ behavior, supply chain processes, internal processes, or any other measurable variable. Data Insights can significantly improve operational efficiencies, streamline processes, and generate higher revenues.

The Challenge, however, is to ensure that the data gathered can be used meaningfully, and the first step to that is a Data Transformation Process. The following are the benefits of the Data Transformation Process in Data Mining:

  • Getting the Most out of Data Interpretation: More than 60% of data for business intelligence goes unanalyzed. Data Transformation makes it possible for businesses to access data for usability by standardizing it.
  • Better Data Management: Businesses are constantly producing data from an increasing number of sources. When there are inconsistencies in the metadata, it can be difficult to organize and comprehend it.
  • Improved Data Management: Inconsistencies in metadata can make it difficult to organize and interpret data as companies collect more data from various sources.
  • Improved Query Performance: Data that has been standardized and is stored in destination databases can be accessed more quickly.
  • Improved Data Quality: Poor data quality is a major issue for organizations when making business decisions. Data Transformation Process in Data Mining, it can help to eliminate inconsistencies and missing values while also improving data quality.


The Data Transformation Process in Data Mining enables businesses and organizations to extract data from various sources and formats and convert it into a useful form that can be used to provide insights. As data from all sources becomes more abundant, there are limitless opportunities to use data to make better business decisions or improve any desired result. The role of Data Transformation is critical in this.

Data Transformation techniques in Data Mining are critical for creating a usable dataset and performing operations such as lookups, adding timestamps, and including geolocation information.

To become more and more efficient in managing your databases, it is preferable to integrate them with a solution that can perform Data Transformation Process in Data Mining procedures.

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