K Means Clustering Quiz

K-Means Clustering

• K-means clustering is a type of unsupervised machine learning algorithm used for partitioning the data into K clusters based on their similarities
• It is commonly used for customer segmentation, image segmentation, anomaly detection, and gene expression analysis

Determining the Number of Clusters

• There is no definitive method to determine the optimal number of clusters (K) in K-means clustering
• The most common approaches to determine K include the elbow method, silhouette analysis, and gap statistic
• The choice of K often depends on the specific problem, data, and domain knowledge

Limitations of K-Means Clustering

• The main limitation of K-means clustering is its sensitivity to the initial placement of centroids and the scales of the features
• It is also sensitive to outliers and noisy data, which can significantly affect the clustering results
• Additionally, K-means clustering assumes spherical clusters, which may not always be the case in real-world datasets