Clustering Techniques and Distance Measures
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Questions and Answers

What is a characteristic of stocks in Cluster 1?

  • Small-cap with low growth potential
  • Stable, consistent returns
  • High returns and high volatility (correct)
  • Regular dividend payments

    • Which type of investors would likely prefer stocks from Cluster 2?

  • Speculative investors looking for volatility
  • Income-focused investors looking for dividends
  • Aggressive investors seeking high returns
  • Conservative investors seeking stable returns (correct)

    • What is one benefit of portfolio diversification based on stock clustering?

  • It only includes stocks from the same cluster.
  • It guarantees high returns.
  • It eliminates all investment risks.
  • It balances high-growth stocks with low-volatility stocks. (correct)

    • What defines Cluster 3 stocks?

    <p>Stocks that provide regular dividend payments</p> Signup and view all the answers

    Which variable is NOT mentioned as part of the healthcare clustering analysis?

    <p>Height</p> Signup and view all the answers

    What is a key characteristic of patients in Cluster 1 of the hospital system's analysis?

    <p>Older patients with high BMI and frequent hospital visits</p> Signup and view all the answers

    How can sector-based analysis be applied after clustering stocks?

    <p>To make investment decisions based on sector performance.</p> Signup and view all the answers

    What is an example of a reason for grouping stocks based on risk characteristics?

    <p>To match different risk profiles with investment strategies.</p> Signup and view all the answers

    What defines a 2D dataset in terms of dimensions?

    <p>A dataset with 2 features.</p> Signup and view all the answers

    Why are algorithms necessary when dealing with high-dimensional data?

    <p>They help measure similarity among data points in any dimension.</p> Signup and view all the answers

    Which of the following is NOT a type of distance measure used for numerical data?

    <p>Jaccard Distance</p> Signup and view all the answers

    What is a typical characteristic of high-dimensional datasets?

    <p>They may consist of hundreds or more features.</p> Signup and view all the answers

    Which distance measure is suitable for binary data?

    <p>Matching Distance</p> Signup and view all the answers

    Which clustering method is NOT mentioned as a valid approach?

    <p>DBSCAN</p> Signup and view all the answers

    What do we need to decide when clustering high-dimensional data?

    <p>The stopping criteria for the clustering algorithm.</p> Signup and view all the answers

    In the context of clustering, what is meant by 'stopping criteria'?

    <p>The point at which the clustering algorithm ceases to refine clusters.</p> Signup and view all the answers

    What is the formula for normalizing a value X using Min-Max normalization?

    <p>NewValue = (X - min) / (max - min)</p> Signup and view all the answers

    Which value represents the normalized income of $45,000 given the minimum income of $10,000 and maximum income of $80,000?

    <p>0.5</p> Signup and view all the answers

    What is the primary reason for transforming binary data during standardization?

    <p>To calculate the mean and standard deviation for the binary data.</p> Signup and view all the answers

    In standardization, what does a NewValue of 0 represent?

    <p>The value is equal to the sample mean.</p> Signup and view all the answers

    Which attributes must be standardized according to the content provided?

    <p>Both continuous and binary variables.</p> Signup and view all the answers

    If the sample mean of ages in a dataset is 24 and an individual's age is 27, what is the standardized value?

    <p>0.25</p> Signup and view all the answers

    What is the purpose of applying Min-Max normalization to a dataset?

    <p>To scale the features within a specific range.</p> Signup and view all the answers

    What is the relationship between Min-Max normalization and standardization in context?

    <p>Both methods transform data into a comparable scale.</p> Signup and view all the answers

    What does Single Linkage in clustering refer to?

    <p>Minimum pairwise distance between points from two different clusters.</p> Signup and view all the answers

    Which method computes the maximum distance between points from two different clusters?

    <p>Complete Linkage</p> Signup and view all the answers

    In clustering, what is the significance of a centroid?

    <p>It serves as the mean point that summarizes the location of all points in the cluster.</p> Signup and view all the answers

    How is Average Linkage computed in clustering?

    <p>By averaging the pairwise distances between points from two different clusters.</p> Signup and view all the answers

    Which linkage method relies on computing the centroid distance between clusters?

    <p>Centroid Linkage</p> Signup and view all the answers

    What assumption can be made regarding points A, B, and C based on direct distance?

    <p>B and C are more likely related based on proximity alone.</p> Signup and view all the answers

    Which statement about clustering is incorrect regarding distance assessment?

    <p>Maximum distance is always preferred for similarity assessment.</p> Signup and view all the answers

    In which scenario would Single Linkage likely falter?

    <p>When clusters are elongated and not compact.</p> Signup and view all the answers

    What is the mean of the numbers 18, 27, and 29?

    <p>24.67</p> Signup and view all the answers

    What is the sample standard deviation of the three data points: 18, 27, and 29?

    <p>5.86</p> Signup and view all the answers

    What standardized score corresponds to the value 18 using the computed mean and standard deviation?

    <p>-1.138</p> Signup and view all the answers

    Which normalization method transforms features to range between 0 and 1?

    <p>Min-Max Normalization</p> Signup and view all the answers

    Which of the following is NOT a distance measure for numerical attributes?

    <p>Cosine Similarity</p> Signup and view all the answers

    What is the main purpose of standardization in data processing?

    <p>To ensure features have a mean of 0 and a standard deviation of 1</p> Signup and view all the answers

    How is standardized score calculated for an individual data point?

    <p>By subtracting the mean and dividing by the standard deviation</p> Signup and view all the answers

    Does the distance between two rows indicate the distance between their corresponding clusters?

    <p>No, distances do not correlate directly</p> Signup and view all the answers

    Study Notes

    Clustering Techniques

    • Clustering is a process of grouping similar data points together.
    • Stocks within the same cluster are considered similar while stocks in different clusters are dissimilar.
    • Clustering algorithms group data based on features/dimensions.
    • In low-dimensional spaces, clusters can be identified by simple plots.
    • In high-dimensional spaces, algorithms are required to measure similarity and identify clusters.

    Distance Measures for Clustering

    • Numerical Data: Euclidean distance and Manhattan distance.
    • Binary Data: Matching distance and Jaccard distance.
    • Categorical Data: No standard measures.
    • Data Normalization: Used to scale and transform data for better clustering.
      • Min-Max Normalization: Transforms each feature to a range between 0 and 1.
      • Standardization: Transforms each feature to have a mean of 0 and a standard deviation of 1.

    Distance Between Clusters

    • Direct distance between data points does not necessarily reflect the distance between their clusters.
    • Linkage Methods are used to measure the distance between clusters:
      • Single Linkage: Minimum pairwise distance between points from two different clusters.
      • Complete Linkage: Maximum pairwise distance between points from two different clusters.
      • Average Linkage: Average pairwise distance between points from two different clusters.
      • Centroid Linkage: Distance between the centroids (cluster means) of two clusters.
      • Ward's Method: Minimizes the variance within each cluster.

    Cluster Centroids

    • The centroid of a cluster represents the "mean point" of the cluster, where each coordinate is the mean value of the corresponding feature.

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    Related Documents

    Cluster Analysis I PDF

    Description

    This quiz covers essential concepts of clustering techniques, including the grouping of similar data points and various distance measures used in clustering algorithms. You will explore how different types of data require different distance calculations and the importance of data normalization. Test your understanding of these foundational concepts in data analysis.

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