Cluster analysis using metaheuristic algorithms has earned increasing popularity over recent years due to the great success of these in finding high-quality clusters complex real-world problems. This paper proposes a novel framework for automatic data clustering with capability generating approximately same maximum distortion nature-inspired binary optimization algorithms. The inherent problem ...

Wireless communication greatly contributes to the evolution of new technologies, such as Internet Things (IoT) and edge computing. The generation networks, including 5G 6G, provide several connectivity advantages for multiple applications, smart health systems cities. Adopting wireless technologies in these applications is still challenging due factors mobility heterogeneity. Predicting accurat...

In real-world scenarios, identifying the optimal number of clusters in a dataset is difficult task due to insufficient knowledge. Therefore, indispensability sophisticated automatic clustering algorithms for this purpose has been contemplated by some researchers. Several assisted quantum-inspired metaheuristics have developed recent years. However, literature lacks definitive documentation stat...

In this work, we attempt to tackle the problem of automatic facial expression recognition using unsupervised clustering algorithms. Our features consist of Active Appearance Model tracked facial interest points. We explore the use of various clustering algorithms like k-means, Gaussian Mixture Models and a novel temporal clustering algorithm Aligned Cluster Analysis [7]. The Extended Cohn-Kanad...

Scalability in monitoring and management of cloud data centers may be improved through the clustering of virtual machines (VMs) exhibiting similar behavior. However, available solutions for automatic VM clustering present some important drawbacks that hinder their applicability to real cloud scenarios. For example, existing solutions show a clear trade-off between the accuracy of the VMs cluste...

In this paper a new method for automatic word clustering is presented. We used this method for building n-gram language models for Persian continuous speech recognition (CSR) systems. In this method, each word is specified by a feature vector that represents the statistics of parts of speech (POS) of that word. The feature vectors are clustered by k-means algorithm. Using this method causes a r...