In clustering ensemble, it is desired to combine several clustering outputs in order to create better results than the output results of the basic individual clustering methods in terms of consistency, robustness and performance. In this research, we want to present a clustering ensemble method with a new aggregation function. The proposed method is named Robust Clustering Ensemble based on Iterative Fusion of Base Clusters (RCEIFBC). This method takes into account the two similarity criteria: (a) one of them is the cluster-cluster similarity and (b) the other one is the object-cluster similarity. The proposed method has two steps and has been done on the binary cluster representation of the given ensemble. Indeed, before doing any step, the primary partitions are converted into a binary cluster representation where the primary ensemble has been broken into a number of primary binary clusters. The first step is to combine the primary binary clusters with the highest cluster-cluster similarity. This phase will be replicated as long as our desired candidate clusters are ready. The second step is to improve the merged clusters by assigning the data points to the merged clusters. The performance and robustness of the proposed method have been evaluated over different machine learning datasets. The experimentation indicates the effectiveness of the proposed method comparing to the state-of-the-art clustering methods in terms of performance and robustness.

In this paper, Proactive Unified Spectrum Handoff (PUSH) scheme in cognitive radio mobile ad hoc networks (CR-MANETs) through an established route is considered in which the channel availability depends on the Primary User's (PU's) activity, secondary user's (SU's) mobility, and the channel heterogeneity. First, an analytical model for Unified Spectrum Handoff (USH) scheme is introduced in which the SUs will move to another unused spectrum band, giving priority to a PU. Then, the PUSH method is proposed in which a handoff threshold is used to initiate the handoff proactively. When a channel handoff cannot be performed due to the SU's mobility, a local flow handoff (LFH) is performed. The proposed PUSH algorithm uses the cognitive link availability prediction while considering the PU interference boundary to estimate the maximum link accessibility period. Based on the analytical model, the channel heterogeneity and the SU's mobility affected the performance of the handoff management method rather than the PU's activity. Both the simulation and analytical results demonstrated an improvement of route maintenance probability based on the LFH and cognitive link availability prediction mechanisms in PUSH.

The paper introduces proactive integrated handoff management (IHM) scheme in cognitive radio mobile ad hoc networks (CR-MANETs) through an established route. This scheme considers the primary user (PU) activity, secondary user (SU) mobility and channel heterogeneity in the spectrum handoff decision. To reduce the handoff delay, handoff thresholds are used. When the spectrum handoff cannot be done due to the SU's mobility, a local flow handoff (LFH) is performed to find a node in the vicinity of a potential link breakage where the data flow is transferred to the node. The proposed proactive IHM scheme predicts the cognitive link availability that considers the interference to PUs to estimate the maximum link availability time. The availability time is then used in the channel allocation scheme. The results emphasized on improvement of the route maintenance probability using LFH. It is also verified that the number of handoff needed is significantly decreased using the proposed proactive IHM scheme.