Cluster Analysis: Types and Purpose
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Questions and Answers

What is the primary purpose of cluster analysis?

  • To test hypotheses about the relationships between variables
  • To visualize the distribution of data
  • To identify sub-groups within the data set that exhibit similar behavior (correct)
  • To identify outliers in the dataset

    • What is a characteristic of cluster analysis?

  • It is an exploratory process that does not require hypotheses (correct)
  • It is a method that is only used for categorical data
  • It is a method that is only used for normally distributed data
  • It is a confirmatory process that tests hypotheses

    • Why is using correlations as a distance measure problematic?

  • Because correlations are only used for categorical data
  • Because correlations measure similarity in variation, not similarity in scores (correct)
  • Because correlations are difficult to calculate
  • Because correlations are sensitive to outliers

    • What is an advantage of using cluster analysis?

    <p>It can handle non-normally distributed data</p> Signup and view all the answers

    What is a limitation of cluster analysis?

    <p>It may not be strongly replicable, meaning different datasets may not produce the same clusters</p> Signup and view all the answers

    What type of cluster analysis is characterized by the formation of a hierarchy of clusters?

    <p>Hierarchical cluster analysis</p> Signup and view all the answers

    What is an example of a scenario where cluster analysis would be useful?

    <p>Identifying sub-groups of consumers with similar purchasing behaviors</p> Signup and view all the answers

    What is a key consideration when choosing a distance measure for cluster analysis?

    <p>The type of data being analyzed</p> Signup and view all the answers

    What is the difference between Euclidian distance and City-block distance?

    <p>Euclidian distance is the hypotenuse, while City-block distance is the sum of non-hypotenuse sides.</p> Signup and view all the answers

    What is the purpose of selecting a distance metric in cluster analysis?

    <p>To measure the distance between data points.</p> Signup and view all the answers

    What is the difference between hierarchical and k-means clustering?

    <p>Hierarchical clustering treats new clusters as single points, while k-means clustering treats new clusters as a collection of points.</p> Signup and view all the answers

    What is the purpose of a proximity matrix in cluster analysis?

    <p>To commence clustering.</p> Signup and view all the answers

    What is the difference between single linkage and complete linkage?

    <p>Single linkage iteratively groups variables with the shortest distance, while complete linkage iteratively groups variables with the furthest distance.</p> Signup and view all the answers

    What is the purpose of determining the number of clusters in cluster analysis?

    <p>To identify the underlying structure of the data.</p> Signup and view all the answers

    What is the difference between two-step clustering and hierarchical clustering?

    <p>Two-step clustering is a combination of hierarchical and k-means clustering methods.</p> Signup and view all the answers

    What is the purpose of a dendrogram in hierarchical clustering?

    <p>To visualize the clustering results.</p> Signup and view all the answers

    Study Notes

    Cluster Analysis

    • Aims to identify sub-groups (clusters) within a dataset, where participants behave similarly
    • Clusters should have higher within-group similarity than between-group similarity

    Purpose of Cluster Analysis

    • An exploratory process, unlikely to have hypotheses in advance about group behavior
    • May not be strongly replicable, and different datasets may yield different clusters
    • A simple method for identifying latent classes of participants

    Why Use Cluster Analysis?

    • Data may not be normally distributed
    • Substantial individual differences may not be captured by means
    • Example: locations of people in Australia, where the mean may not represent the data accurately

    Distance Measures/Metrics

    • Correlations measure similar variation, not similar scores
    • Instead, seek similarity in actual values using distance metrics
    • Types of distance metrics:
      • Euclidean distance (hypotenuse)
      • City-block distance (taxi-cab geometry, sum of non-hypotenuse sides)

    Cluster Analysis Process

    • Select a distance metric (e.g., block, squared Euclidean, Euclidean)
    • Commence clustering using a proximity matrix
    • Choose a clustering method:
      • Hierarchical
      • K-means
      • Two-step
    • Combine clusters using methods such as:
      • Nearest neighbor (single linkage or shortest distance)
      • Furthest neighbor (complete linkage or furthest distance)

    Hierarchical Clustering

    • Dendrogram: a graphical representation of the clustering process
    • Treat new clusters as single points rather than collections of points

    K-means Clustering

    • Output: clusters and centroids
    • Different from hierarchical clustering in approach

    Two-Step Clustering

    • See summary below for details

    Determining the Number of Clusters

    • Hierarchical clustering: use the dendrogram to determine the number of clusters
    • K-means clustering: output provides information on the number of clusters

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    Description

    Understanding cluster analysis, including distance measures, types of cluster analysis, and its purpose in identifying sub-groups within a data-set.

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