In recent years, the usage of wireless devices and wireless service has been increased exponentially and it results in spectrum scarcity. The policies of regulatory authority employ static spectrum allocation methods and assign new spectrum band for offering new kind of services to the users. These approaches lead to poor utilization of available spectrum bands. The cognitive radio (CR) provides better solution to these problems and it mainly focuses on efficient utilization of available spectrum bands. CR network (CRN) has to adopt spectrum management techniques to assign the unused spectrum band to the CR users by following a sequence of actions such as spectrum sensing, decision and management. Spectrum sensing is a vital process in spectrum allocation. In the traditional approaches, sensing accuracy is brought down by the probabilities of misdetection and false alarm rate and hence, sensing accuracy becomes low. As a result, the Cognitive Users (CUs) face the challenge of prolonged time to complete perfect cognitive radio communication. To overcome this issue, a cooperative spectrum sensing technique with a characteristic based cluster classifier has been proposed. This classifier learns the states and their transitions in the radio frequency environment, as well as the primary user activities at regular time intervals to support the spectrum decision technique. The novelty of the work is to propose a hybrid approach which combines clustering with expected maximization (EM) algorithm and reinforcement learning (RL) techniques. This hybrid approach enhances the system performance with accurate sensing results and by identifying the optimum spectrum band through hierarchical access model using interweaving approach, energy consumption is minimized. The simulation results show that by decreasing the probabilities of error ratio, false alarm rate and missed detection, the accuracy of sensing results is improved. Further, this hybrid approach outperforms the traditional approaches in terms of probability of detection even in low SNR values. © 2020 Elsevier B.V.