Methods in Health Statistics: Integration of Multiple Imputation in Cluster Analysis

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What is the aim of the proposed framework discussed in the lesson?

To learn about multiple imputation in cluster analysis
To integrate missing data sets in a cluster analysis using k-means algorithm
To describe the impact of missing data on uncertainty in deciding the optimal number of clusters
All of the above (correct)

Which method is integrated in the cluster analysis as per the lesson?

Principal component analysis
k-means algorithm (correct)
Hierarchical clustering
Factor analysis

What is the main focus when applying multiple imputation to a data set with missing data?

Creating additional variables
Reducing the dimensionality of the data
Identifying the reasons for missing data
Estimating missing values (correct)

In what context is the optimal number of clusters determined?

Impact of missing data on uncertainty (D) Signup and view all the answers

Which algorithm is used for the cluster analysis with integrated multiple imputation?

k-means algorithm (D) Signup and view all the answers

What license is this work released under?

Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License (A) Signup and view all the answers

What is the main advantage of using multiple imputation over complete case analysis in the presence of missing data?

It is a proper alternative to complete cases analysis and reduces bias (D) Signup and view all the answers

What is the k-means clustering algorithm designed to do?

Classify data elements into homogeneous unknown groups (B) Signup and view all the answers

Why is finding the optimal clustering by performing an exhaustive search of all possible partitions not computationally feasible?

Because it reduces the search but is not guaranteed to reach a global solution (C) Signup and view all the answers

What is a relevant issue when applying any clustering algorithm to high-dimensional data?

The number of individuals relative to the number of variables (B) Signup and view all the answers

What is the main difficulty in choosing the best subset of variables for cluster analysis?

Comparing two clustering classifications based on different numbers of variables (A) Signup and view all the answers

How can the optimal number of clusters and final set of variables be selected according to CritCF?

Using a backward sequential selection algorithm (C) Signup and view all the answers

What does high CritCF values indicate when selecting the optimal number of clusters and clustering variables?

Greater relevance in clustering (D) Signup and view all the answers

What is the primary purpose of multiple imputation?

To address missing data (C) Signup and view all the answers

Cluster analysis is the process whereby data elements are classified into:

Homogeneous unknown groups based on characteristics (C) Signup and view all the answers

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)?

Because it deteriorates the distance-based criteria for comparisons (A) Signup and view all the answers

What is used to compare the fit of two classifications with different numbers of clusters?

Penalization for the value of k (A) Signup and view all the answers

What does CritCF rank partitions based on?

Different numbers of clusters and different numbers of variables (C) Signup and view all the answers