5. Jaccard coefficient is used in clustering when the variables are___________
Question
- Jaccard coefficient is used in clustering when the variables are___________
Solution
The Jaccard coefficient is used in clustering when the variables are categorical.
Here's a step-by-step explanation:
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The Jaccard coefficient is a measure of similarity between two sets. It's calculated as the size of the intersection divided by the size of the union of the two sets.
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In the context of clustering, it's used to measure the similarity between different clusters.
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When the variables are categorical, it means that they can take on one of a limited, and usually fixed, number of possible values. For example, the color of a car could be a categorical variable, with possible values of red, blue, green, etc.
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The Jaccard coefficient is particularly useful for categorical variables because it doesn't require any kind of numerical computation on the variables themselves. It simply looks at whether the categories match (i.e., are in the intersection) or not.
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Therefore, when the variables are categorical, the Jaccard coefficient can be used in clustering to measure the similarity between different clusters.
Similar Questions
11. For the following vectors, 𝑥 = (0, 1, 0, 1,1) 𝑎𝑛𝑑 𝑦 = (1, 0, 1, 0,1)1. Calculate Simple Matching Coefficient2. Calculate Euclidean3. Calculate Jaccard Coefficient
Jaccard’s coefficient is different from the matching coefficient in that the formerGroup of answer choicesdoes not count matching zero entries while the latter does.is affected by the scale used to measure variables while the latter is not.measures overlap while the latter measures dissimilarity.deals with categorical variable while the latter deals with continuous variables.
In k-means clustering, k represents the
The cluster profile of a cluster generated using clustering should be illustrated based on ___________________.Group of answer choicesthe interpretation of the domain expertthe relative distributions and cluster comparisons of the clustering solutionthe important relative importance of the variablesthe variable of interest
What is the output of the K-means clustering algorithm?Two dimensional representation of the data and the clustersCenter of each discovered cluster and mapping of each record to a clusterCentroid positioning and entropy of each record in each clusterIntercept and coefficients for each input variable in the dataset
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