ML-K-Medoids Algorithm

In unsupervised machine learning, which type of algorithm is K-medoids?

• Clustering algorithm

• Classification algorithm

• Regression algorithm

• Reinforcement learning algorithm

What is the main objective of the K-medoids algorithm?

• To minimize the variance within clusters

• To maximize the number of clusters

• To predict the target variable of a dataset

• To identify the most central data points as cluster representatives

K-medoids is an improvement over K-means, especially when dealing with:

• Small datasets

• Large datasets

• Categorical data

• Outliers

What is the primary difference between K-means and K-medoids?

• K-means uses medians as cluster representatives, while K-medoids uses means.

• K-medoids assigns each data point to the nearest cluster centroid, while K-means assigns data points to the nearest medoid.

• K-medoids is a density-based clustering algorithm, while K-means is prototype-based.

• K-means can handle categorical data, while K-medoids can only handle numerical data.

What does PAM stand for in the context of clustering algorithms?

• Partition Around Medoids

• Prototype Analysis and Medoids

• Point Allocation Method

• Proximity-based Association Measures

Which step of the PAM algorithm is responsible for iteratively swapping medoids to improve clustering?

• Partitioning step

• Assigning step

• Swapping step

• Initialization step

What is the complexity of the PAM algorithm compared to the K-means algorithm?

• PAM is computationally more efficient than K-means.

• PAM and K-means have the same computational complexity.

• PAM is computationally less efficient than K-means.

• The computational complexity of PAM depends on the dataset size

What does the term "medoid" refer to in the context of K-medoids and PAM algorithms?

• The mean value of the data points within a cluster.

• The most central data point in a cluster.

• The data point with the highest variance in a cluster.

• The first data point in a cluster.

Which step in the K-medoids algorithm is responsible for updating the medoids in each iteration?

• Initialization step

• Swapping step

• Partitioning step

• Assigning step

The PAM algorithm aims to minimize what measure during the clustering process?

• The sum of squared distances within clusters

• The variance between clusters

• The number of clusters formed

• The mean value of the data points