Data Transformation in Data Mining
Last Updated :
12 Feb, 2025
Data transformation in data mining refers to the process of converting raw data into a format that is suitable for analysis and modeling. It also ensures that data is free of errors and inconsistencies. The goal of data transformation is to prepare the data for data mining so that it can be used to extract useful insights and knowledge.
Data Transformation Techniques
The data transformation involves various methods that are:
1. Smoothing
It is a process that is used to remove noise from the dataset using some algorithms It allows for highlighting important features present in the dataset. It helps in predicting the patterns. When collecting data, it can be manipulated to eliminate or reduce any variance or any other noise form. The concept behind data smoothing is that it will be able to identify simple changes to help predict different trends and patterns. This serves as a help to analysts or traders who need to look at a lot of data which can often be difficult to digest for finding patterns that they wouldn't see otherwise.
2. Aggregation
Data collection or aggregation is the method of storing and presenting data in a summary format. The data may be obtained from multiple data sources to integrate these data sources into a data analysis description. This is a crucial step since the accuracy of data analysis insights is highly dependent on the quantity and quality of the data used. Gathering accurate data of high quality and a large enough quantity is necessary to produce relevant results. The collection of data is useful for everything from decisions concerning financing or business strategy of the product, pricing, operations, and marketing strategies. For example, Sales, data may be aggregated to compute monthly and annual total amounts.
Read more about Aggregation in detail.
3. Discretization
It is a process of transforming continuous data into set of small intervals. Most Data Mining activities in the real world require continuous attributes. Yet many of the existing data mining frameworks are unable to handle these attributes. Also, even if a data mining task can manage a continuous attribute, it can significantly improve its efficiency by replacing a constant quality attribute with its discrete values. For example, (1-10, 11-20) (age:- young, middle age, senior).
Read more about Discretization in detail.
4. Attribute Construction
Where new attributes are created and applied to assist the mining process from the given set of attributes. This simplifies the original data and makes the mining more efficient.
5. Generalization
It converts low-level data attributes to high-level data attributes using concept hierarchy. For Example Age initially in Numerical form (22, 25) is converted into categorical value (young, old). For example, Categorical attributes such as house addresses, may be generalized to higher-level definitions, such as town or country.
6. Normalization
Data normalization involves converting all data variables into a given range. Techniques that are used for normalization are:
- Min-Max Normalization:
- This transforms the original data linearly.
- Suppose that: min_A is the minima and max_A is the maxima of an attribute
- v is the value you want to plot in the new range.
- v' is the new value you get after normalizing the old value.
v' = (v - min_A) / (max_A - min_A)
- Z-Score Normalization:
- In z-score normalization (or zero-mean normalization) the values of an attribute (A), are normalized based on the mean of A and its standard deviation
- A value v of attribute A is normalized to v' by computing using below formula-
v' = (v - mean(A)) / (standard deviation(A))
- Decimal Scaling:
- It normalizes the values of an attribute by changing the position of their decimal points
- The number of points by which the decimal point is moved can be determined by the absolute maximum value of attribute A.
- A value, v, of attribute A is normalized to v' by computing
- where j is the smallest integer such that Max(|v'|) < 1.
- Suppose: Values of an attribute P varies from -99 to 99.
- The maximum absolute value of P is 99.
- For normalizing the values we divide the numbers by 100 (i.e., j = 2) or (number of integers in the largest number) so that values come out to be as 0.98, 0.97 and so on.
Read about Normalization in detail.
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