Questions and Answers
What is the aim of the proposed framework discussed in the lesson?
All of the above
Which method is integrated in the cluster analysis as per the lesson?
k-means algorithm
What is the main focus when applying multiple imputation to a data set with missing data?
Estimating missing values
In what context is the optimal number of clusters determined?
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Which algorithm is used for the cluster analysis with integrated multiple imputation?
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What license is this work released under?
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What is the main advantage of using multiple imputation over complete case analysis in the presence of missing data?
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What is the k-means clustering algorithm designed to do?
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Why is finding the optimal clustering by performing an exhaustive search of all possible partitions not computationally feasible?
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What is a relevant issue when applying any clustering algorithm to high-dimensional data?
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What is the main difficulty in choosing the best subset of variables for cluster analysis?
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How can the optimal number of clusters and final set of variables be selected according to CritCF?
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What does high CritCF values indicate when selecting the optimal number of clusters and clustering variables?
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What is the primary purpose of multiple imputation?
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Cluster analysis is the process whereby data elements are classified into:
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Why can adding more variables to an analysis degrade the final classification if the number of individuals (n) is small relative to the number of variables (p)?
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What is used to compare the fit of two classifications with different numbers of clusters?
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What does CritCF rank partitions based on?
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