Data Transformation in Data Mining

In this tutorial, we will learn about the data transformation in data mining, data transformation or data scaling techniques. By Palkesh Jain Last updated : April 17, 2023

Data Transformation

Data transformation is a process used to turn raw data into an acceptable format that allows data mining in order to effectively and quickly extract strategic information. It is impossible to track or interpret raw data, which is why it has to be pre-processed before any data is extracted from it. In order to turn the data into the required shape, data processing involves data cleaning techniques as well as a data reduction strategy. Data is generalized and normalized. Normalization is a system that guarantees that no knowledge is obsolete, that all is stored in a single location, and that all the dependencies are logical.In order to include patterns that are easier to interpret, data transformation is one of the important data pre-processing strategies that must be done on data before data mining.

Data Transformation or Data Scaling Techniques

1. Data Cleaning

Cleaning the data implies eliminating noise from the collection of data considered. There, using techniques like binning, regression, clustering.

2. Attribute Construction

For data elimination, methods for data transformation may also be used. If we build a new function that blends the features to make the process of data mining more effective, it is called a choice of attributes.

For example, male/female and student traits may be built into male/female students. When we do studies into how many men and/or women are teachers, this may be helpful, but we are not involved in their field of study.

3. Data Aggregation

Aggregation of data is any mechanism by which data is compiled and expressed in a summarized form. Atomic data rows - usually obtained from different sources are replaced by totals or summary figures as data are aggregated. Based on such results, classes of observed aggregates are replaced with summary statistics. Usually, aggregate data is found in a data center, as it can offer responses to technical queries and thus significantly minimize the time for massive data sets to be queried. In a reasonable time frame, data aggregation will allow analysts to view and analyze vast volumes of data. Hundreds, thousands, or even more atomic data records may be represented by a row of aggregate data. Instead of having all of the computation cycles to access each underlying atomic data row and aggregate it in real-time as it is queried or downloaded, it can be easily queried when the data is aggregated.

4. Data Normalization

Normalization allows us to scale the data within a range when practicing and/or performing data analysis to prevent constructing inaccurate ML models. It would be difficult to compare the numbers if the data set is very wide. We can transform the original data linearly; perform decimal scaling or Z-score normalization with different normalization techniques.

5. Discretization

The numeric attributes' raw values are replaced by discrete or logical intervals, which can be further grouped into higher-level intervals in return.

6. Generation of concept hierarchy for nominal data

Values are extended to higher-order concepts for nominal data.

Advantages of Data Transformation

The following are the advantages of data transformation:

  • To make things better-organised, data is transformed. It could be simpler for both humans and computers to use transformed results.
  • Properly formatted and validated data increases the consistency of data and defends implementations from possible landmines, such as null values, unintended duplicates, inaccurate indexing, and incompatible formats.
  • Compatibility between programs, processes, and data forms is enabled by data transformation. It may be important to convert data used for various uses in different ways.

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