K-Means ClusteringΒΆ
Global Algorithm - One-Dimensional Algorithm
K-Means Clustering algorithm is a simple unsupervised learning algorithm used to solve clustering problems. By assuming \(k\) clusters, it minimizes the sum of distances (points to cluster centroids) through iteration.
For details refer to the online tutorial http://www-2.cs.cmu.edu/~awm/tutorials/kmeans.html.
Input Parameters
| Parameter | Type | Constraint | Description | Remarks |
|---|---|---|---|---|
| \(Y\) | \(Y \in \mathbb R^{N}\) | \(N \in \mathbb{N}\) | Input data of size \(N\) | |
| \(k\) | \(k \in \mathbb{N}\) | \(k \lt N\) | Specified number of clusters |
Output Parameters
| Parameter | Type | Constraint | Description | Remarks |
|---|---|---|---|---|
| \(\hat{Y}\) | \(\hat{Y} \in \mathbb R^{k}\) | A vector of \(k\) cluster centroid locations |
Tool Support
Single Steps using the Algorithm
- Data Discretization with K-Means Clustering
- Data Reduction with K-Means Clustering
- Outlier Detection with K-Means Clustering
References
- J.B. MacQueen, Some Methods for classification and Analysis of Multivariate Observations, Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, Berkeley, University of California Press, vol. 1, pp. 281-297, 1967.