Nowadays, we live in a world in which people are facing with a lot of data that should be stored or displayed. One of the key methods to control and manage this data refers to grouping and classifying them in clusters. Today, clustering has a critical role in information retrieval methods for organizing large collections inside a few significant clusters. One of the main motivations for the use of clustering is to determine and reveal the hidden and inherent structure of a set of data. Ensemble clustering algorithms combine multiple clustering algorithms to finally reach an overall clustering system. Ensemble clustering methods by lack of information fusing utilize several primary partitions of data to find better ways. Since various clustering algorithms look at the different data points, they can produce various partitions from such data. It is possible to create a partition with high performance by combining the partitions obtained from different algorithms, even if the clusters to be very dense from each other. Most studies in this area have examined all the initial clusters. In this study, a new method is used in which the most sustainable clusters are utilized instead of all primary produced clusters. Consensus function based on co-association matrixes used to select more stable clusters. The most stable clusters selection method is done by cluster stability criterion based on F-measure. Optimization functions are used to optimize the obtained final clusters. The genetic algorithm is the optimizer used in this article to find the ultimate clusters participated in a consensus. Experimental results on several datasets show that the output of proposed method is various clusters with high stability.

Internet of things (IoT) is a network of smart things. This indicates the ability of these physical things to transfer information with other physical things. The characteristics of these networks, such as topology dynamicity and energy constraint, challenges the routing problem in these networks. Previous routing methods could not achieve the required performance in this type of network. Therefore, developers of this network designed and developed specific methods in order to satisfy the requirements of these networks. One of the routing methods is utilization of multipath protocols which send data to its destination using routes with separate links. One of such protocols is RPL routing protocol. In this paper, this method is improved using composite metrics which chooses the best paths used for separate routes to send packets. We propose Energy and Load aware RPL (ELaM-IoT) protocol, which is an enhancement of RPL protocol. It uses a composite metric, calculated based on remaining energy, hop count, Link Expiration Time (LET), load and battery depletion index (BDI) for the route selection. In order to evaluate and report the results, the proposed ELaM-IoT method is compared to the ERGID and ADRM-IoT approaches with regard to average remaining energy, and network lifetime. The results demonstrate the superior performance of the proposed ELaM-IoT compared to the ERGID and ADRM-IoT approaches.