*Machine Learning Algorithms from Wireless Sensor Network's Perspective DOI: http://dx.doi.org/10.5772/intechopen.111417*

objects are divided into separate clusters using an unsupervised learning algorithm, which also serves as a useful technique for automatically identifying group categories in unlabelled datasets without the need for training. As each cluster is linked to a centroid, the method is centroid-based. The main objective of this approach is to minimise the overall distances between the data points and the clusters they belong to. Because it is straightforward and linear in complexity, the K-means clustering algorithm is used for clustering WSN sensor nodes and is useful for finding the cluster heads as well.

The K-means method is demonstrated in the phases below:

