How do we classify data points based on their proximity to the centroids in the custom k-means algorithm?
In the custom k-means algorithm, data points are classified based on their proximity to the centroids. This process involves calculating the distance between each data point and the centroids, and then assigning the data point to the cluster with the closest centroid. To classify the data points, the algorithm follows these steps: 1. Initialization: The
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Clustering, k-means and mean shift, Custom K means, Examination review
What is the difference between k-means and mean shift clustering algorithms?
The k-means and mean shift clustering algorithms are both widely used in the field of machine learning for clustering tasks. While they share the goal of grouping data points into clusters, they differ in their approaches and characteristics. K-means is a centroid-based clustering algorithm that aims to partition the data into k distinct clusters. It
- Published in Artificial Intelligence, EITC/AI/MLP Machine Learning with Python, Clustering, k-means and mean shift, K means with titanic dataset, Examination review
What is the limitation of the k-means algorithm when clustering differently sized groups?
The k-means algorithm is a widely used clustering algorithm in machine learning, particularly in unsupervised learning tasks. It aims to partition a dataset into k distinct clusters based on the similarity of data points. However, the k-means algorithm has certain limitations when it comes to clustering differently sized groups. In this answer, we will consider