CS 312 AI Clustering Algorithms

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Questions and Answers

What is the primary goal of the k-means clustering algorithm?

To equalize similarity across all clusters

To minimize within-cluster variances (correct)

To increase the number of clusters

To maximize within-cluster variances

What does the term 'centroid' refer to in k-means clustering?

A process for selecting the number of clusters

The average distance of data points in a cluster

A data point that represents the cluster's center (correct)

The sum of all Euclidean distances within a cluster

Which step in the k-means algorithm involves assigning data points to clusters?

Centroid selection step

Expectation step (correct)

Minimization step

Maximization step

What method is used to evaluate the quality of cluster assignments in k-means clustering?

Sum of squared errors (SSE) Signup and view all the answers

What characteristic is NOT desired in k-means clustering between different clusters?

High similarity between clusters Signup and view all the answers

How is the new centroid calculated in the k-means algorithm?

By computing the mean of all the points for each cluster Signup and view all the answers

What does the value of k represent in k-means clustering?

The number of clusters to be formed Signup and view all the answers

What is the significance of the initialization of centroids in the k-means algorithm?

It affects the final clustering result and computational efficiency Signup and view all the answers

What does the Elbow method help determine when choosing the number of clusters?

The optimal number of clusters based on SSE Signup and view all the answers

What happens to the SSE as more clusters are added using the Elbow method?

SSE decreases as k increases Signup and view all the answers

What does the silhouette coefficient measure in clustering?

The similarity of data points within a cluster Signup and view all the answers

What range of values can the silhouette coefficient take?

-1 to 1 Signup and view all the answers

What significance does the elbow point have in the Elbow method?

It represents a good trade-off between error and number of clusters Signup and view all the answers

Which method evaluates how well a data point fits into its assigned cluster by comparing its distance to points in other clusters?

Silhouette method Signup and view all the answers

Which of the following describes what occurs at the elbow point during the Elbow method analysis?

The reduction in SSE becomes less significant Signup and view all the answers

Which of these factors is NOT considered when calculating the silhouette coefficient?

Distance to the farthest point in the cluster Signup and view all the answers

What does hierarchical clustering primarily create for categorizing data?

A dendrogram Signup and view all the answers

In hierarchical clustering, which policy involves starting with individual samples and merging them into groups?

Bottom-up policy Signup and view all the answers

What does the root in a hierarchical clustering dendrogram represent?

The only cluster of all samples Signup and view all the answers

What is the result of 'cutting' the dendrogram at a specified depth?

Creation of k groups of smaller dendrograms Signup and view all the answers

Which type of hierarchical clustering divides clusters into smaller groups rather than merging them?

Divisive clustering Signup and view all the answers

What kind of clustering structure is most commonly used in hierarchical clustering?

Tree-like structure Signup and view all the answers

Which of the following statements accurately describes the leaves of a dendrogram in hierarchical clustering?

They represent clusters of single samples Signup and view all the answers

Which of the following best defines a dendrogram?

A diagram that shows the structure of hierarchical clustering Signup and view all the answers

Study Notes

CS 312 Introduction to Artificial Intelligence: Clustering Algorithms

Machine Learning Algorithm Overview: Machine learning algorithms are categorized into supervised learning (classification, regression), unsupervised learning (clustering), and other methods.
Clustering Algorithms: These algorithms group similar data points together. Unsupervised learning algorithms are used to automatically classify unlabeled data.
k-means Clustering: This algorithm takes the number of clusters (k) and a dataset as input, producing k clusters with minimized within-cluster variances. High similarity within clusters and low similarity between clusters are key characteristics. This algorithm uses expectation-maximization (two-step): expectation step assigns points to nearest centroid; maximization step computes new centroids.
k-means Algorithm Steps:
- Specify the number of clusters (k).
- Randomly initialize k centroids.
- Repeat until centroids don't change:
  - Assign each point to its closest centroid.
  - Compute new centroids (mean of each cluster).
Choosing the Appropriate Number of Clusters (k):
- Elbow Method: Plots SSE (Sum of Squared Errors) against k. The 'elbow' point suggests a good trade-off between error and the number of clusters.
- Silhouette Coefficient: A value between -1 and 1. Higher values represent better-defined clusters. Higher values indicate samples are closer to their own clusters than to others.
Hierarchical Clustering: Creates a tree-like structure called a dendrogram, where clusters are formed at different levels. There are two types of Hierarchical clustering:
- Agglomerative: Bottom-up approach, where similar data points are merged into clusters.
- Divisive: Top-down approach, where a large cluster is split into smaller clusters at each stage.
Density-Based Clustering: Identifies clusters based on the density of data points in a region. This approach finds clusters of arbitrary shapes, unlike k-means which typically finds spherical clusters.
Reporting for Next Meeting:
- Assigned Reporter 1: Provide sample code for k-means clustering, showing the method used to choose the number of clusters (k).
- Assigned Reporters 3: Discuss Density-based clustering, compare it to k-means and hierarchical clustering, and present sample code for the three clustering algorithms with a common dataset, comparing and interpreting the results of each approach.

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Description

This quiz explores clustering algorithms within the context of CS 312 Introduction to Artificial Intelligence. It covers the basics of machine learning, specifically focusing on unsupervised learning techniques like k-means clustering. You'll learn about the steps involved in the k-means algorithm and its key characteristics.