What is the primary objective of the K-means clustering algorithm?
Understand the Problem
The question is asking about the main goal of the K-means clustering algorithm, which is a method used in data analysis to partition data into distinct groups (clusters) based on their similarities.
Answer
Minimize the distances between data points and their respective cluster centroids.
The primary objective of the K-means clustering algorithm is to minimize the sum of distances between data points and their respective cluster centroids, grouping similar data points into clusters.
Answer for screen readers
The primary objective of the K-means clustering algorithm is to minimize the sum of distances between data points and their respective cluster centroids, grouping similar data points into clusters.
More Information
K-means clustering is a widely used algorithm in data science and machine learning for partitioning datasets into clusters. Each cluster is represented by its centroid, and the algorithm works iteratively to minimize the distance between data points and their respective centroid.
Tips
A common mistake is choosing the wrong number of clusters (k), which can lead to poor clustering results. It is often useful to test multiple values of k to find the best fit.
Sources
- K means Clustering - Introduction - GeeksforGeeks - geeksforgeeks.org
- K-Means Clustering- Introduction - Analytics Vidhya - analyticsvidhya.com
- K means Clustering Algorithm - Simplilearn.com - simplilearn.com
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