K mean cluster

What does K represent in K means Clustering ?

• Number of Data Points

• Number of Iterations Before Algorithm Stops

• Number of Clusters

• Number of Features

What are some applications of unsupervised learning?

• Customer segmentation, Image compression, News Classification

• Data visualization, Performance estimation, Keyword suggestion

• Face clustering, Search result clustering, Clustering in search advertising

• Learn clusters/groups without any label, Bioinformatics: learn motifs, Find important features

What is clustering in the context of unsupervised learning?

• The process of grouping a set of objects into classes of similar objects

• The process of training a model using labeled examples

• The process of labeling data points with predefined categories

• The process of predicting future outcomes based on historical data

Which distance measure is commonly used in the K-means clustering algorithm?

• Euclidean distance

• Cosine similarity

• Manhattan distance

• Hamming distance

What is the purpose of the K-means loss function in the clustering process?

• To minimize the number of clusters in the dataset

• To balance the distribution of data points across clusters

• To maximize the distance between data points and cluster centers

• To minimize the sum of squared distances from each point to its associated cluster center

What is a common issue with the K-means algorithm?

• It is sensitive to the initial choice of cluster centers

• It requires labeled data for training

• It cannot handle high-dimensional data

• It always converges to the global minimum

Which parameter affects the convergence of the K-means algorithm?

• Random seed selection

• Maximum number of iterations

• Distance measure

• Number of clusters

What is the purpose of the 'mean-shift clustering' algorithm?

• To assign each example to exactly one cluster

• To shift data points towards regions of higher density

• To perform hierarchical clustering

• To calculate the mean of the points in each cluster

How does the 'furthest centers heuristic' help in initializing the K-means algorithm?

• It selects cluster centers that are closest to each other

• It selects cluster centers that are furthest from each other

• It calculates the mean of all data points

• It randomly assigns cluster centers

Which Python library can be used to implement the K-means clustering algorithm?

• TensorFlow

• Matplotlib

• PyTorch

• Scikit-learn