15 Questions
What is the main issue with the k-means algorithm when dealing with outliers?
An object with an extremely large value may substantially distort the distribution of the data.
What is the main difference between the k-means algorithm and the k-medoids algorithm?
The k-means algorithm uses the mean value of the objects in a cluster as a reference point, whereas the k-medoids algorithm uses the most centrally located object (medoid) in a cluster.
What is the main goal of the PAM (K-Medoids) algorithm?
To find the optimal set of medoids that minimize the total distance of the resulting clustering.
What is the limitation of the PAM algorithm?
It does not scale well for large data sets due to the computational complexity.
What is the main objective of the k-medoid clustering method?
To find representative objects (medoids) in clusters.
What is the primary objective of unsupervised learning in the context of cluster analysis?
Learning by observations without predefined classes.
What is the characteristic of a good clustering method in terms of intra-class and inter-class similarity?
High intra-class similarity and low inter-class similarity.
What is the purpose of a distance function in clustering?
To express similarity between data objects.
Why is it important to associate weights with different variables in clustering?
To account for differences in application and data semantics.
What is the significance of a 'quality' function in clustering?
It measures the 'goodness' of a cluster.
How does k-means clustering differ from supervised learning?
K-means is an unsupervised learning method, whereas supervised learning involves predefined classes.
What is the primary goal of cluster analysis?
Finding similarities between data objects and grouping them into clusters.
What is the role of similarity measures in clustering?
To determine the similarity or dissimilarity between data objects.
How do the characteristics of a cluster affect the quality of clustering?
The quality of clustering depends on the intra-class similarity and inter-class similarity of the clusters.
What is the significance of clustering in data analysis?
Clustering helps to identify patterns or relationships in data by grouping similar objects together.
Explore the key considerations for cluster analysis, including partitioning criteria, separation of clusters, and similarity measures. Learn about the different types of partitioning and how to choose the right approach for your data. Discover the importance of similarity measures in clustering.
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