K-means is a popular clustering algorithm used in unsupervised machine learning. Its main objective is to partition a given dataset into k clusters, where k is a user-defined parameter. The algorithm tries to minimize the sum of squared distances between each data point and the centroid of the cluster it belongs to.
The algorithm starts by randomly selecting k data points whatsapp mobile number list from the dataset as the initial centroids. Then, each data point is assigned to the cluster whose centroid is closest to it. After that, the centroids are updated by computing the mean of all data points belonging to each cluster. This process of assigning data points to clusters and updating centroids is repeated until the centroids no longer change or a maximum number of iterations is reached.
K-means has a number of advantages. First, it is computationally efficient and can handle large datasets. Second, it is easy to implement and interpret. Third, it is effective at identifying spherical clusters with similar sizes. However, K-means also has some limitations. One of the major challenges is that the algorithm is sensitive to the initial selection of centroids. A poor initialization can lead to suboptimal results, and the algorithm may get stuck in local minima. Additionally, K-means assumes that clusters are spherical, equally sized and densely packed, which may not be the case in real-world datasets.
To evaluate the quality of the resulting clusters, there are several metrics that can be used, including the Silhouette coefficient, which measures the similarity of a data point to its own cluster compared to other clusters. A higher Silhouette score indicates better clustering.
In conclusion, K-means is a popular and effective clustering algorithm with several advantages and limitations. Its main objective is to partition a given dataset into k clusters by minimizing the sum of squared distances between each data point and the centroid of the cluster it belongs to. To evaluate the quality of the clusters, several metrics can be used, including the Silhouette coefficient.