Data Normalization Techniques and Concepts

This video tutorial covers four data normalization techniques in data mining: min-max normalization, z-score normalization using mean and standard deviation, z-score normalization using mean absolute deviation, and decimal scaling. Each technique is explained with examples, demonstrating how to apply them to a dataset. The video aims to provide a clear understanding of these methods and their applications in data mining.

What is the primary purpose of data normalization in data mining?

• To increase data size

• To reduce data redundancy

• To standardize data values

• To encrypt data

Which of the following is NOT a data normalization technique mentioned in the video?

• Logarithmic Scaling

• Min-Max Normalization

• Decimal Scaling

• Z-score Normalization

In Min-Max normalization, what is the formula used to calculate the normalized value?

• (x - Min) / (Max - Min)

• (x - Mean) / Standard Deviation

• x / 10^J

• (x - Mean) / Mean Absolute Deviation

What is the normalized value of 200 using Min-Max normalization if the minimum value is 200 and the maximum value is 1000?

• 0

• 0.25

• 0.5

• 1

Which equation is used in Z-score normalization with mean and standard deviation?

• z = (x - Min) / (Max - Min)

• z = (x - Mean) / Standard Deviation

• z = (x - Mean) / Mean Absolute Deviation

• z = x / 10^J

What is the mean of the data set 200, 300, 400, 600, and 1000?

• 700

• 600

• 500

• 400

What is the standard deviation of the data set 200, 300, 400, 600, and 1000?

• 400

• 300

• 200

• 282.8

In Z-score normalization using mean and mean absolute deviation, what replaces the standard deviation?

• Mode

• Variance

• Mean Absolute Deviation

• Median

What is the mean absolute deviation of the data set 200, 300, 400, 600, and 1000?

• 200

• 240

• 400

• 300

In decimal scaling normalization, what is the smallest integer J for the data point 200?

• 1

• 2

• 3

• 4