Spiking neural networks (SNNs) are the closest approach to biological neurons in comparison with conventional artificial neural networks (ANN). SNNs are composed of neurons and synapses which are interconnected with a complex pattern. As communication in such massively parallel computational systems is getting critical, the network-on-chip (NoC) becomes a promising solution for providing a scalable and robust interconnection fabric. However, using NoC for large-scale SNNs arises a trade-off among scalability, throughput, neuron/router ratio (cluster size), and area overhead. In this paper, we tackle the trade-off using clustering approach and try to optimize the synaptic resources utilization. An optimal cluster size can provide lowest area overhead and power consumption. For the learning purposes, a phenomenon known as spike-timing-dependent plasticity (STDP) is utilized. The micro-architectures of the network, clusters, and the computational neurons are also described. The presented approach suggests a promising solution of integrating NoCs and STDP-based SNNs for the optimal performance based on the underlying application.