K-Means is a widely used clustering algorithm that partitions a set of data points into K clusters, where each cluster is defined by its centroid. The goal of the algorithm is to minimize the sum of squared distances between each data point and its closest centroid.
The algorithm starts by randomly selecting K initial centroids and assigning each data point to the closest centroid. Then, it iteratively updates the position of the centroids and reassigns each data point to the closest centroid until the assignments no longer change. The algorithm terminates when the centroids reach a stable position.
The mathematical intuition behind K-Means is based on the concept of minimizing the within-cluster sum of squared errors (WCSS). WCSS is a measure of the similarity between the data points in a cluster and the cluster’s centroid. The goal of the algorithm is to minimize the WCSS by finding the optimal centroid positions.
In mathematical terms, the WCSS can be represented as:
WCSS = ∑i=1k ∑x∈Ci (x – μi)^2
Where k is the number of clusters, C is the set of data points in a cluster, and μ is the centroid of the cluster.
K-Means is sensitive to the initial centroid positions, so it’s important to run the algorithm multiple times with different initial centroids. To overcome this limitation, researchers have proposed variations of the algorithm such as K-Means++, which uses a more sophisticated method of selecting initial centroids.
K-Means is a simple yet powerful algorithm that can be applied to various types of data. However, it has some limitations, such as assuming that clusters are spherical and equally sized, which may not always be the case. Additionally, it doesn’t work well with categorical data or data with the non-uniform distribution.
In conclusion, K-Means is a popular clustering algorithm that partitions a set of data points into K clusters by minimizing the WCSS. It’s a simple, easy-to-implement, and computationally efficient algorithm that can be applied to various types of data, but it also has some limitations.
For the practical implementation of the K-Means Clustering Algorithm, please visit my GitHub.