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HFS-based computational method for weighted fuzzy time series forecasting model using techniques of adaptive radius clustering and grey wolf optimization
Recently, hesitant fuzzy sets (HFSs) have been used extensively in time series forecasting. HFSs have inherent characteristics of addressing problem...
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ARDOD: adaptive radius density-based outlier detection
Outlier detection has garnered considerable attention in recent years due to its wide-ranging applications across various research domains. This...
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Adaptive encoding-based evolutionary approach for Chinese document clustering
Document clustering has long been an important research direction in intelligent system. When being applied to process Chinese documents, new...
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ACRA: Adaptive meta-heuristic based Clustering and Routing Algorithm for IoT-assisted wireless sensor network
Opportunistic routing is crucial for the development and management of an efficient and flexible network in Internet of Things (IoT) assisted...
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An intermittent fault diagnosis method of analog circuits based on variational modal decomposition and adaptive dynamic density peak clustering
Analog circuits are widely used in industrial systems and avionics. Intermittent faults (IFs) as a special type of fault in circuits are difficult to...
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Adaptive Clustering—An Optimal Solution to Hotspot Issue in Wireless Sensor Networks
In wireless sensor networks, maximization of lifetime is an important task. Load balancing with clustering is an energy efficient approach for...
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The Research of 3D Point Cloud Data Clustering Based on MEMS Lidar for Autonomous Driving
In the field of autonomous driving, the perception of the environment plays a crucial role, serving as a fundamental component. Accurate and precise...
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Density-based anti-clustering for scheduling D2D communications
Wireless link scheduling in device-to-device (D2D) networks is an NP-hard problem. As a solution, multiple supervised deep learning (DL) models have...
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Clustering
This chapter provides a comprehensive overview of traditional clustering algorithms, which have been fundamental in the field of unsupervised...
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An Agile Adaptive Clustering Algorithm for Wireless Sensor Networks Considering Energy Constraint
Today wireless sensor networks are present in all aspects of human life, such as medicine, industry, transportation, and agriculture. Energy...
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Investigating the influence of clustering techniques and parameters on a hybrid PSO-driven ANFIS model for electricity prediction
The availability of reliable electrical power, which is essential for a comfortable lifestyle worldwide, requires realistic power usage projections...
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Improvement and application of information communication technology in wireless routing protocol based on adaptive K-means clustering algorithm
With the continuous development of information and communication network technology and data mining technology, an ICT routing algorithm based on...
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An improved black hole algorithm designed for K-means clustering method
Data clustering has attracted the interest of scholars in many fields. In recent years, using heuristic algorithms to solve data clustering problems...
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An energy efficient grid-based clustering algorithm using type-3 fuzzy system in wireless sensor networks
The efficient management of energy in wireless sensor networks (WSNs) is a primary concern among researchers. Clustering algorithms serve as a...
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Decentralized Sparse Gaussian Process Regression with Event-Triggered Adaptive Inducing Points
In this paper, we present a decentralized sparse Gaussian process regression (DSGPR) model with event-triggered, adaptive inducing points. We address...
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Prediction of WEDM Performances Using Clustering Techniques in ANFIS During Machining of A286 Superalloy
Wire Electric Discharge Machining (WEDM) involves a high degree of nonlinearity and stochastic phenomena due to its complexity and process...
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Adaptive spatiotemporal dimension reduction in concurrent multiscale damage analysis
Concurrent multiscale damage models are often used to quantify the impacts of manufacturing-induced micro-porosity on the damage response of...
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A novel density peaks clustering algorithm based on K nearest neighbors with adaptive merging strategy
Recently the density peaks clustering algorithm (DPC) has received a lot of attention from researchers. The DPC algorithm is able to find cluster...
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An Adaptive Clustering Approach for Distributed Outlier Detection in Data Streams
Many real-world problems deal with collections of high-dimensional data, i.e., data with many different features. A dataset exhibiting a high number...
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A dynamic core evolutionary clustering algorithm based on saturated memory
Because the number of clustering cores needs to be set before implementing the K-means algorithm, this type of algorithm often fails in applications...
