An enhanced K-Means clustering technique with Hopfield Artificial Neural Network based on reactive clustering protocol

An efficient algorithm is presented in this paper to enhance the lifetime of WSN and to become the network more energy efficient. In wireless sensor networks, due to the enhancement in the quantity of data, it becomes very complex to analyze those data, Categorize those data into singular collection. This may leads to the requirement for better data mining techniques. One of the mostly used clustering techniques is K-Means clustering. This paper proposed a new technique to enhance the K-Means clustering, which can result in better performance. For initialization, this paper uses an improved version of Hopfield Artificial Neural Network (HANN) algorithm. Also Reactive networks, is in combined with the k-means clustering, as opposed to proactive networks, Immediately it May Respond to changes in relevant parameters of interest. The experimental result indicates that the proposed K-Means clustering algorithm gives the better results as compared to the other techniques.