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An Improved Boosting Bald Eagle Search Algorithm with Improved African Vultures Optimization Algorithm for Data Clustering
Data clustering is one of the main issues in the optimization problem. It is the process of clustering a group of items into several groups. Items...
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Data fusion algorithm of wireless sensor network based on clustering and fuzzy logic
In order to reduce network energy consumption and prolong the network lifetime in wireless sensor networks, a data fusion algorithm named CFLDF is...
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GTIACO: energy efficient clustering algorithm based on game theory and improved ant colony optimization
Recently, wireless sensor networks have been widely used for environmental and structural safety monitoring. However, node batteries cannot be...
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Augmented arithmetic optimization algorithm using opposite-based learning and lévy flight distribution for global optimization and data clustering
This paper proposes a new data clustering method using the advantages of metaheuristic (MH) optimization algorithms. A novel MH optimization...
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An energy efficient clustering with enhanced chicken swarm optimization algorithm with adaptive position routing protocol in mobile adhoc network
In mobile ad hoc networks (MANETs), when nodes sporadically visit every path, limiting battery life and leading to frequent topology changes,...
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An Improved K-means Clustering Algorithm Towards an Efficient Data-Driven Modeling
K-means algorithm is one of the well-known unsupervised machine learning algorithms. The algorithm typically finds out distinct non-overlap**...
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Mixed integer linear programming formulation for K-means clustering problem
The minimum sum-of-squares clusering is the most widely used clustering method. The minimum sum-of-squares clustering is usually solved by the...
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Orthogonal nonnegative matrix factorization problems for clustering: A new formulation and a competitive algorithm
Orthogonal Nonnegative Matrix Factorization (ONMF) with orthogonality constraints on a matrix has been found to provide better clustering results...
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Fuzzy clustering of financial time series based on volatility spillovers
In this paper we propose a framework for fuzzy clustering of time series based on directional volatility spillovers. In the case of financial time...
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Comparison of empirical and shrinkage correlation algorithm for clustering methods in the futures market
The correlation structure of the futures market obtained using daily data from 2009 to 2020 has been investigated to show how different sectors, such...
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FLAG: fuzzy logic augmented game theoretic hybrid hierarchical clustering algorithm for wireless sensor networks
Stability of the wireless sensor network (WSN) is the most critical factor in real-time and data-sensitive applications like military and...
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A Summary of User Profile Research Based on Clustering Algorithm
Clustering algorithm is applicable to calculate and analyze the potential characteristics of users’ data. The results of clustering can analyze the...
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Geometry-Inference Based Clustering Heuristic: New k-means Metric for Gaussian Data and Experimental Proof of Concept
K-means is one of the algorithms that are most utilized in data clustering; the number of metrics is coupled to k-means to reach reasonable levels of...
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Improving Bayesian Classifier Using Vine Copula and Fuzzy Clustering Technique
Classification is a fundamental problem in statistics and data science, and it has garnered significant interest from researchers. This research...
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Cub model-based clustering of Likert-type data with a tourist satisfaction application
In investigating customer satisfaction with products or services, the most popular approach still relies on interviews or questionnaires to obtain...
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EELCR: energy efficient lifetime aware cluster based routing technique for wireless sensor networks using optimal clustering and compression
Wireless sensor networks (WSNs) offer a multitude of advantages and find applications across various domains, garnering substantial research...
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A novel auto-pruned ensemble clustering via SOCP
Operations Research (OR) plays a crucial role in strategic decision-making in today’s business world; it uses complex algorithms and data analytic to...
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A fair-multicluster approach to clustering of categorical data
In the last few years, the need of preventing classification biases due to race, gender, social status, etc. has increased the interest in designing...
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Trust Cop-Kmeans Clustering Method
Social network large-scale decision-making (SNLSDM) has attracted widespread attention in the field of decision science. Clustering is one of the...
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A typology of social innovation: A comparative study of clustering methodologies
This study offers a typology of the Social Innovation (SI) field. A sample of 5,152 documents from the Scopus database is screened using a clustering...
