When clustering only by dummy variables that represent categorical variables, the simplest measure of similarity between two observations is called the "Hamming distance".

Step 1: Convert categorical variables into binary (0,1) dummy variables. Each category of each variable becomes a new binary variable.

Step 2: Calculate the Hamming distance between two observations. The Hamming distance is the number of positions at which the corresponding values are different. In the context of dummy variables, it is the number of variables for which one observation is 1 and the other observation is 0.

Step 3: Use the Hamming distance as the measure of dissimilarity in a clustering algorithm. The smaller the Hamming distance, the more similar the two observations

Question

When clustering only by dummy variables that represent categorical variables, the simplest measure of similarity between two observations is called the "Hamming distance".

Step 1: Convert categorical variables into binary (0,1) dummy variables. Each category of each variable becomes a new binary variable.

Step 2: Calculate the Hamming distance between two observations. The Hamming distance is the number of positions at which the corresponding values are different. In the context of dummy variables, it is the number of variables for which one observation is 1 and the other observation is 0.

Step 3: Use the Hamming distance as the measure of dissimilarity in a clustering algorithm. The smaller the Hamming distance, the more similar the two observations

Knowee AI · Accepted Answer

When clustering only by dummy variables that represent categorical variables, the simplest measure of similarity between two observations is called the "Hamming distance".

Step 1: Convert categorical variables into binary (0,1) dummy variables. Each category of each variable becomes a new binary variable.

Step 2: Calculate the Hamming distance between two observations. The Hamming distance is the number of positions at which the corresponding values are different. In the context of dummy variables, it is the number of variables for which one observation is 1 and the other observation is 0.

Step 3: Use the Hamming distance as the measure of dissimilarity in a clustering algorithm. The smaller the Hamming distance, the more similar the two observations

When clustering only by dummy variables that represent categorical variables, the simplest measure of similarity between two observations is called the ...

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When clustering only by dummy variables that represent categorical variables, the simplest measure of similarity between two observations is called the ...

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