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Questions and Answers
What is a characteristic of stocks in Cluster 1?
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?
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?
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?
What defines Cluster 3 stocks?
Which variable is NOT mentioned as part of the healthcare clustering analysis?
Which variable is NOT mentioned as part of the healthcare clustering analysis?
What is a key characteristic of patients in Cluster 1 of the hospital system's analysis?
What is a key characteristic of patients in Cluster 1 of the hospital system's analysis?
How can sector-based analysis be applied after clustering stocks?
How can sector-based analysis be applied after clustering stocks?
What is an example of a reason for grouping stocks based on risk characteristics?
What is an example of a reason for grouping stocks based on risk characteristics?
What defines a 2D dataset in terms of dimensions?
What defines a 2D dataset in terms of dimensions?
Why are algorithms necessary when dealing with high-dimensional data?
Why are algorithms necessary when dealing with high-dimensional data?
Which of the following is NOT a type of distance measure used for numerical data?
Which of the following is NOT a type of distance measure used for numerical data?
What is a typical characteristic of high-dimensional datasets?
What is a typical characteristic of high-dimensional datasets?
Which distance measure is suitable for binary data?
Which distance measure is suitable for binary data?
Which clustering method is NOT mentioned as a valid approach?
Which clustering method is NOT mentioned as a valid approach?
What do we need to decide when clustering high-dimensional data?
What do we need to decide when clustering high-dimensional data?
In the context of clustering, what is meant by 'stopping criteria'?
In the context of clustering, what is meant by 'stopping criteria'?
What is the formula for normalizing a value X using Min-Max normalization?
What is the formula for normalizing a value X using Min-Max normalization?
Which value represents the normalized income of $45,000 given the minimum income of $10,000 and maximum income of $80,000?
Which value represents the normalized income of $45,000 given the minimum income of $10,000 and maximum income of $80,000?
What is the primary reason for transforming binary data during standardization?
What is the primary reason for transforming binary data during standardization?
In standardization, what does a NewValue of 0 represent?
In standardization, what does a NewValue of 0 represent?
Which attributes must be standardized according to the content provided?
Which attributes must be standardized according to the content provided?
If the sample mean of ages in a dataset is 24 and an individual's age is 27, what is the standardized value?
If the sample mean of ages in a dataset is 24 and an individual's age is 27, what is the standardized value?
What is the purpose of applying Min-Max normalization to a dataset?
What is the purpose of applying Min-Max normalization to a dataset?
What is the relationship between Min-Max normalization and standardization in context?
What is the relationship between Min-Max normalization and standardization in context?
What does Single Linkage in clustering refer to?
What does Single Linkage in clustering refer to?
Which method computes the maximum distance between points from two different clusters?
Which method computes the maximum distance between points from two different clusters?
In clustering, what is the significance of a centroid?
In clustering, what is the significance of a centroid?
How is Average Linkage computed in clustering?
How is Average Linkage computed in clustering?
Which linkage method relies on computing the centroid distance between clusters?
Which linkage method relies on computing the centroid distance between clusters?
What assumption can be made regarding points A, B, and C based on direct distance?
What assumption can be made regarding points A, B, and C based on direct distance?
Which statement about clustering is incorrect regarding distance assessment?
Which statement about clustering is incorrect regarding distance assessment?
In which scenario would Single Linkage likely falter?
In which scenario would Single Linkage likely falter?
What is the mean of the numbers 18, 27, and 29?
What is the mean of the numbers 18, 27, and 29?
What is the sample standard deviation of the three data points: 18, 27, and 29?
What is the sample standard deviation of the three data points: 18, 27, and 29?
What standardized score corresponds to the value 18 using the computed mean and standard deviation?
What standardized score corresponds to the value 18 using the computed mean and standard deviation?
Which normalization method transforms features to range between 0 and 1?
Which normalization method transforms features to range between 0 and 1?
Which of the following is NOT a distance measure for numerical attributes?
Which of the following is NOT a distance measure for numerical attributes?
What is the main purpose of standardization in data processing?
What is the main purpose of standardization in data processing?
How is standardized score calculated for an individual data point?
How is standardized score calculated for an individual data point?
Does the distance between two rows indicate the distance between their corresponding clusters?
Does the distance between two rows indicate the distance between their corresponding clusters?
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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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