Data Discretization
Data Discretization is the process of converting continuous data into discrete intervals or categories. It is a common technique used in data preprocessing to reduce the complexity of data and make it easier to analyze. Discretization can be done using various methods such as equal width binning, equal frequency binning, and clustering. Equal width binning divides the range of values into equal-sized intervals, while equal frequency binning divides the data into intervals with equal number of data points. Clustering methods group similar data points together to form intervals. Discretization can be useful in various applications such as data mining, machine learning, and decision making. It can help to reduce noise in data, improve accuracy of models, and simplify data visualization. However, it can also lead to loss of information and introduce bias in the data analysis process.
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