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3. Given the following data for attribute age: 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70 Use all types of binning method for data smoothing in order to solve the above data.

3. Given the following data for attribute age: 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70 Use all types of binning method for data smoothing in order to solve the above data.

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
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Data Mining

### Problem 3: Data Smoothing Using Binning Methods **Given Data for Attribute Age:** 13, 15, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 25, 30, 33, 33, 33, 35, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70 **Task:** Use all types of **binning methods for data smoothing** to solve the above data. --- In this exercise, you are to apply various binning methods to smooth the given dataset of ages. Binning is a preprocessing technique used to reduce the effects of minor observation errors by grouping a number of more-or-less continuous values into a smaller number of "bins". This can enhance the stability of statistical analyses and reporting. ### Explanation of Binning Methods 1. **Equal-Width Binning:** - Divide the range of data into intervals of equal size. - The number of bins is typically determined before the process begins. 2. **Equal-Depth Binning (Equal-Frequency Binning):** - Each bin contains approximately the same number of samples or data points. - This can be useful for data with uneven distribution. 3. **Smoothing by Bin Mean:** - Each bin is replaced by the mean value of the bin. - This helps eliminate noise by smoothing the values. 4. **Smoothing by Bin Median:** - Each bin is replaced by the median value of the bin. - This method is resistant to outliers, providing more robust smoothing. 5. **Smoothing by Bin Boundaries:** - The minimum and maximum values of a bin are used to replace the bin values. - It reduces the impact of noise and outliers at the boundaries. These methods help in understanding and preparing data for further analysis, ensuring that it is clean, accurate, and free from significant noise or anomalies.
Transcribed Image Text:### Problem 3: Data Smoothing Using Binning Methods **Given Data for Attribute Age:** 13, 15, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 25, 30, 33, 33, 33, 35, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70 **Task:** Use all types of **binning methods for data smoothing** to solve the above data. --- In this exercise, you are to apply various binning methods to smooth the given dataset of ages. Binning is a preprocessing technique used to reduce the effects of minor observation errors by grouping a number of more-or-less continuous values into a smaller number of "bins". This can enhance the stability of statistical analyses and reporting. ### Explanation of Binning Methods 1. **Equal-Width Binning:** - Divide the range of data into intervals of equal size. - The number of bins is typically determined before the process begins. 2. **Equal-Depth Binning (Equal-Frequency Binning):** - Each bin contains approximately the same number of samples or data points. - This can be useful for data with uneven distribution. 3. **Smoothing by Bin Mean:** - Each bin is replaced by the mean value of the bin. - This helps eliminate noise by smoothing the values. 4. **Smoothing by Bin Median:** - Each bin is replaced by the median value of the bin. - This method is resistant to outliers, providing more robust smoothing. 5. **Smoothing by Bin Boundaries:** - The minimum and maximum values of a bin are used to replace the bin values. - It reduces the impact of noise and outliers at the boundaries. These methods help in understanding and preparing data for further analysis, ensuring that it is clean, accurate, and free from significant noise or anomalies.
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