The advent of architectures such as the Internet of Things (IoT) has led to the dramatic
growth of data and the production of big data. Managing this often-unlabeled data is a big …

Ensemble clustering has emerged as a combination of several basic clustering algorithms to
achieve high quality final clustering. However, this technique is challenging due to the …

The ever-increasing growth of the number of Internet of Things (IoT) devices connected to
the network has led to the emergence of cloud computing shortcomings such as delay …

Compliance with security requirements in Wireless Sensor Networks (WSNs) is known as an
important phenomenon in conserving energy consumption due to its dynamic topology …

Electric power load pattern recognition from various accumulated load data is performed for
energy efficiency improvement, power system operation support, and demand side …

Abstract Agglomerative Hierarchical Clustering (AHC) is a bottom-up clustering strategy in
which each object is originally a cluster, and more pairs of clusters are formed by traversing …

Recently, both ensemble clustering and semi-supervised clustering have emerged as
important paradigms of traditional clustering. Ensemble clustering seeks to integrate multiple …

Ensemble clustering combines the results of multiple individual clustering methods for better
results. Basically, all available clustering methods can be combined to produce final …

In hybrid clustering, several basic clustering is first generated and then for the clustering
aggregation, a function is used in order to create a final clustering that is similar to all the …

By combining centrality measures and community detection, a better insight into the nature
of the evolution of important nodes in complex networks is obtained. Meanwhile, the …