Journals Proceedings

Abstract

A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions and to cooperatively pass their data through the network to a Base Station. Due to the increase in the quantity of data across the world, it turns out to be very complex task for analyzing those data. Categorize those data into remarkable collection is one of the common forms of understanding and learning. This leads to the requirement for better data mining technique. These facilities are provided by a standard data mining technique called Clustering. Clustering can be considered the most important unsupervised learning technique so as every other problem of this kind; it deals with finding a structure in a collection of unlabeled data. This paper reviews four types of clustering techniques- K-Means Clustering, LEACH, HEED, and TEEN. K-Means clustering is very simple and effective for clustering. It is appropriate when the large dataset is used for clustering. Simulated study and the experiment results are also presented in this paper.