In wireless sensor networks (WSNs), energy efficiency is the key concern. The clustering of networks is one of the most common strategies to make WSNs energy efficient. In clustering, the nodes are assembled into some virtual clusters and leaders are identified for each cluster called cluster-heads. The normal nodes send their sensed data to the cluster head, then, the cluster head transmits it to the base station after the data aggregation. In most of the developed clustering strategies, the spatial correlation has not been used to filter sensors. Thus in this paper, a correlation model is formulated to filter the sensor nodes on the grounds of the correlation value before engaging in the clustering phase. This correlation model is employed to divide the network into correlated regions. The region where multiple sensors possess an identical copy of event data because of close spatial proximity is known as a correlated region. To pick out a couple of sensors from every correlated region a node selection algorithm is proposed. The selected sensor nodes further engage in the clustering process and remaining nodes switch to sleep mode. This paper also presents a Fuzzy-based distributed cluster technique. The results are compared to familiar CHEF and LEACH techniques. A study is conducted to show the impact of the correlation threshold value on the lifetime of the network and found that the lifespan of the network may be extended by changing the correlation threshold.