Data Transformation in Data Mining
Data transformation in data mining refers to the process of converting raw data into a format that is suitable for analysis and modeling. 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 typically involves several steps, including:
- Data cleaning: Removing or correcting errors, inconsistencies, and missing values in the data.
- Data integration: Combining data from multiple sources, such as databases and spreadsheets, into a single format.
- Data normalization: Scaling the data to a common range of values, such as between 0 and 1, to facilitate comparison and analysis.
- Data reduction: Reducing the dimensionality of the data by selecting a subset of relevant features or attributes.
- Data discretization: Converting continuous data into discrete categories or bins.
- Data aggregation: Combining data at different levels of granularity, such as by summing or averaging, to create new features or attributes.
- Data transformation is an important step in the data mining process as it helps to ensure that the data is in a format that is suitable for analysis and modeling, and that it is free of errors and inconsistencies. Data transformation can also help to improve the performance of data mining algorithms, by reducing the dimensionality of the data, and by scaling the data to a common range of values.
The data are transformed in ways that are ideal for mining the data. The data transformation involves steps 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& annual total amounts.
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).
4. Attribute Construction: Where new attributes are created & applied to assist the mining process from the given set of attributes. This simplifies the original data & 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, P
- Where v is the value you want to plot in the new range.
- v’ is the new value you get after normalizing the old value.
- 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
- 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.
ADVANTAGES OR DISADVANTAGES:
Advantages of Data Transformation in Data Mining:
- Improves Data Quality: Data transformation helps to improve the quality of data by removing errors, inconsistencies, and missing values.
- Facilitates Data Integration: Data transformation enables the integration of data from multiple sources, which can improve the accuracy and completeness of the data.
- Improves Data Analysis: Data transformation helps to prepare the data for analysis and modeling by normalizing, reducing dimensionality, and discretizing the data.
- Increases Data Security: Data transformation can be used to mask sensitive data, or to remove sensitive information from the data, which can help to increase data security.
- Enhances Data Mining Algorithm Performance: Data transformation can improve the performance of data mining algorithms by reducing the dimensionality of the data and scaling the data to a common range of values.
Disadvantages of Data Transformation in Data Mining:
- Time-consuming: Data transformation can be a time-consuming process, especially when dealing with large datasets.
- Complexity: Data transformation can be a complex process, requiring specialized skills and knowledge to implement and interpret the results.
- Data Loss: Data transformation can result in data loss, such as when discretizing continuous data, or when removing attributes or features from the data.
- Biased transformation: Data transformation can result in bias, if the data is not properly understood or used.
- High cost: Data transformation can be an expensive process, requiring significant investments in hardware, software, and personnel.
Overfitting: Data transformation can lead to overfitting, which is a common problem in machine learning where a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new unseen data.
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