ML-K_Means Algorithm

K-Means is a popular clustering algorithm used in unsupervised machine learning. What is the main objective of the K-Means algorithm?

• To minimize the variance within clusters

• To maximize the number of clusters

• To predict the target variable of a dataset

• To classify data into predefined categories

In the K-Means algorithm, what does "K" represent?

• The number of clusters to be formed

• The total number of data points in the dataset

• The sum of squared distances within the clusters

• The variance of the dataset

Which step of the K-Means algorithm involves randomly initializing the cluster centroids?

• Assigning data points to the nearest centroid

• Updating the centroids based on the mean of the data points in each cluster

• Calculating the sum of squared distances within the clusters

• Randomly selecting the number of clusters (K)

What is the measure used in the K-Means algorithm to determine the distance between data points and cluster centroids?

• Euclidean distance

• Pearson correlation

• Cosine similarity

• Mahalanobis distance

What is the main drawback of the K-Means algorithm?

• It is computationally expensive for large datasets.

• It can only handle numerical data and not categorical features.

• It requires the number of clusters (K) to be known in advance.

• It cannot handle outliers in the data.

Which of the following is a commonly used method to determine the optimal number of clusters (K) in K-Means?

• Elbow method

• Silhouette score

• Mean-Shift

• Hierarchical clustering

During the K-Means algorithm, how are data points assigned to clusters in each iteration?

• Randomly

• Based on their similarity to cluster centroids

• Based on the order of data points in the dataset

• Based on their variance within the cluster

What is the objective function that K-Means seeks to minimize?

• Total sum of squared distances between data points and cluster centroids

• Total sum of squared distances between data points within each cluster

• Total sum of squared distances between clusters

• Total number of clusters formed

The K-Means algorithm converges when:

• The centroids of the clusters stabilize and do not change between iterations.

• The number of clusters (K) equals the number of data points.

• The variance of the dataset becomes zero.

• The data points are uniformly distributed across all clusters.

In K-Means, what is the role of the cluster centroid?

• It represents the most distant data point in the cluster.

• It serves as the mean of all data points in the cluster.

• It is the sum of all data points in the cluster.

• It is the index of the cluster.