Nama : Sultan Chairul
NIM : 2581484737
Assigment 5
- Describe the steps of the k-Means clustering algorithm. What does the ‘k’ represent?
- Why is dimensionality reduction useful in machine learning?
- Provide a real-world application for clustering (e.g., customer segmentation).
- What is the main objective of PCA?
Answer :
1. Describe the steps of the k-Means clustering algorithm. What does the ‘k’ represent?
K-Means works by selecting \(k\) initial centroids, assigning each data point to the nearest centroid, and recalculating the centroids based on the assigned points. This process repeats until the clusters stabilize. The value \(k\) represents the number of clusters.
2. Why is dimensionality reduction useful in machine learning?
Dimensionality reduction reduces the number of features in a dataset, making data easier to process, reducing computational costs, and helping remove irrelevant information.
3. Provide a real-world application for clustering.
A retail business can use clustering to group customers based on their purchasing habits. This helps the business create more targeted marketing strategies.
4. What is the main objective of PCA?
The main objective of Principal Component Analysis (PCA) is to reduce the number of dimensions in a dataset while preserving as much important information or variance as possible.
Keterangan : 100%
Bukti : Sudah mengerjakan tugas dengan baik dan benar.
Terimakasih.