The traditional k-means algorithm is widely used in large-scale data clustering because of its easy implementation and efficient process, but it also suffers from the disadvantages of local optimality and poor robustness. In this study, a Csk-means a...
The main influencing factors of the clustering effect of the k-means algorithm are the selection of the initial clustering center and the distance measurement between the sample points. The traditional k-mean algorithm uses Euclidean distance to meas...
As big data continues to evolve, cluster analysis still has a place. Among them, the K-means algorithm is the most widely used method in the field of clustering, which can cause unstable clustering results due to the random selection of the initial c...
Most of data set can be represented in an asymmetric matrix. How to mine the uncertain information from the matrix is the primary task of data processing. As a typical unsupervised learning method, three-way k-means clustering algorithm uses core reg...
Witnessing the tremendous development of machine learning technology, emerging machine learning applications impose challenges of using domain knowledge to improve the accuracy of clustering provided that clustering suffers a compromising accuracy ra...
Oversampling is common and effective in resolving the classification problem of imbalanced data. Traditional oversampling methods are prone to generating overlapping or noisy samples. Clustering can effectively alleviate the above problems to a certa...
Clustering algorithms have the characteristics of being simple and efficient and can complete calculations without a large number of datasets, making them suitable for application in noise reduction processing for audio module mass production testing...
K-means clustering is an important and popular technique in data mining. Unfortunately, for any given dataset (not knowledge-base), it is very difficult for a user to estimate the proper number of clusters in advance, and it also has the tendency of...
Clustering is one of the most significant applications in the big data field. However, using the clustering technique with big data requires an ample amount of processing power and resources due to the complexity and resulting increment in the cluste...
This article presents a classification method that utilizes impedance spectrum features and an enhanced K-means algorithm for Lithium-ion batteries. Additionally, a parameter identification method for the fractional order model is proposed, which is...
Reasonable semantic partition of indoor areas can improve space utilization, optimize property management, and enhance safety and convenience. Existing algorithms for such partitions have drawbacks, such as the inability to consider semantics, slow c...
Aiming at enhancing the accuracy and reliability of velocity calculation in vision navigation, an improved method is proposed in this paper. The method integrates Mask-R-CNN (Mask Region-based Convolutional Neural Network) and K-Means with the pyrami...
Chinese fir (Cunninghamia lanceolata) is a major timber species in China, and obtaining and monitoring the parameters of Chinese fir plantations is of great practical significance. With the help of the K-means algorithm and UAV-LiDAR data, the effici...
With the increasing complexity of patrol tasks, the use of deep reinforcement learning for collaborative coverage path planning (CPP) of multi-mobile robots has become a new hotspot. Taking into account the complexity of environmental factors and ope...
This study proposes an optimal scheduling method for complex integrated energy systems. The proposed method employs a heuristic algorithm to maximize its energy, economy, and environment indices and optimize the system operation plan. It uses the k-m...
To address the issue of excessive grid-connected power fluctuations in wind farms, this paper proposes a capacity optimization method for a hybrid energy storage system (HESS) based on wind power two-stage decomposition. First, considering the suscep...
The precision of short-term photovoltaic power forecasts is of utmost importance for the planning and operation of the electrical grid system. To enhance the precision of short-term output power prediction in photovoltaic systems, this paper proposes...
This study investigates the application of an improved multi-threshold method based on the K-means algorithm for clutter filtering in W-band cloud and fog radar observations. Utilizing W-band millimeter-wave cloud and fog radar data collected from Ma...
Power big data-based artificial intelligence or data mining methods, which can be used to analyze electricity consumption behavior, have been widely applied to provide targeted marketing services for electricity consumers. However, the traditional cl...
: Image segmentation is an important part of ore particle size detection, and the quality of image segmentation directly affects the accuracy and reliability of particle size detection. Due to the poor quality and low efficiency of ore particle image...
To address the insufficient global search efficiency of the original Whale Optimization Algorithm (WOA), this paper proposes an enhanced variant (ImWOA) integrating three strategies. First, a dynamic cluster center-guided search mechanism based on K-...
Sailing speed is a critical factor affecting the ship’s energy consumption and operating costs for a voyage. Inland waterways present a complex navigation environment due to their narrow channels, numerous curved segments, and significant varia...
In recent years, distributed generation (DG) has developed rapidly. Renewable energy, represented by wind energy and solar energy, has been widely studied and utilized. At present, most distributed generators follow the principle of “installati...
To solve the overload problem caused by the high proportion of renewable energy into the power system, it is particularly important to find a suitable distribution network planning scheme. Existing studies have effectively reduced the planning cost b...
The high proportion of distributed photovoltaic (DPV) access has changed the traditional distribution network structure and operation mode, posing a huge threat to the stable operation and economy of the distribution network. Aiming at a reasonable a...
In recent decades, human brain tumor detection has become one of the most challenging issues in medical science. In this paper, we propose a model that includes the template-based K means and improved fuzzy C means (TKFCM) algorithm for detecting hum...
Efficient clustering of high-spatial-dimensional satellite image datasets remains a critical challenge, particularly due to the computational demands of spectral distance calculations, random centroid initialization, and sensitivity to outliers in co...
With the rapid development of world shipping, oil spill accidents such as tanker collisions, illegal sewage discharges, and oil pipeline ruptures occur frequently. As the SAR system expands from single polarization to multipolarization, the Polarmetr...
The energy use analysis of coal-fired power plant units is of significance for energy conservation and consumption reduction. One of the most serious problems attributed to Chinese coal-fired power plants is coal waste. Several units in one plant may...
Traditional information retrieval systems return a ranked list of results to a user’s query. This list is often long, and the user cannot explore all the results retrieved. It is also ineffective for a highly ambiguous language such as Arabic. The mo...
As a classic positioning algorithm with a simple principle and low computational complexity, the trilateration positioning algorithm utilizes the coordinates of three anchor nodes to determine the position of an unknown node, which is widely applied...
Olive cultivation over the past few years has spread across Mediterranean countries with Spain being the world’s largest olive producer among them. Because olives are a major part of the economy for such countries keeping records of their tree...
In this paper, we propose an enhanced clustering protocol that integrates an improved K-means with a Mobility-Aware Cluster Head-Election Scored (IK-MACHES) algorithm, designed for extending the lifetime and operational efficiency of Wireless Sensor...
In order to rationally lay out the location of automobile maintenance service stations, a method of location selection of maintenance service stations based on vehicle trajectory big data is proposed. Taking the vehicle trajectory data as the demand...
The Angular Bisector Insertion Constructive Heuristic Algorithm (ABIA), though effective for small-scale TSPs, suffers from reduced solution quality and high computational complexity in larger instances due to the degradation of its geometric propert...
The K-means algorithm is a well-known and widely used clustering algorithm due to its simplicity and convergence properties. However, one of the drawbacks of the algorithm is its instability. This paper presents improvements to the K-means algorithm...
Metaheuristic algorithms have been hybridized with the standard K-means to address the latter’s challenges in finding a solution to automatic clustering problems. However, the distance calculations required in the standard K-means phase of the...
The weighted K-means clustering algorithm is widely recognized for its ability to assign varying importance to features in clustering tasks. This paper introduces an enhanced version of the algorithm, incorporating a bi-partitioning strategy to segre...
With rapid economic and demographic growth, traffic conditions in medium and large cities are becoming extremely congested. Numerous metropolitan management organizations hope to promote the coordination of traffic and urban development by formulatin...
Automatic clustering problems require clustering algorithms to automatically estimate the number of clusters in a dataset. However, the classical K-means requires the specification of the required number of clusters a priori. To address this problem,...
Using random projection, a method to speed up both kernel k-means and centroid initialization with k-means++ is proposed. We approximate the kernel matrix and distances in a lower-dimensional space Rd before the kernel k-means clustering motivated by...
Wireless sensor networks (WSN) have found more and more applications in remote control and monitoring systems. Energy management in the network is crucial because all nodes in the WSN are energy constrained. Therefore, the design and implementation o...
Many researchers have proposed algorithms to improve the network performance of vehicular ad hoc network (VANET) clustering techniques for different applications. The effectiveness of the clustering model is the most important challenge. The K-Means...
The multilevel fast multipole algorithm (MLFMA) using K-means clustering to accelerate electromagnetic scattering analysis for large complex targets is presented. By replacing the regular cube grouping with the K-means clustering, the addition theore...
K-Means is a “de facto” standard clustering algorithm due to its simplicity and efficiency. K-Means, though, strongly depends on the initialization of the centroids (seeding method) and often gets stuck in a local sub-optimal solution. K-...
K-means clustering algorithm is a partitional clustering algorithm that has been used widely in many applications for traditional clustering due to its simplicity and low computational complexity. This clustering technique depends on the user specifi...
With the development of cloud computing, interest in database outsourcing has recently increased. However, when the database is outsourced, there is a problem in that the information of the data owner is exposed to internal and external attackers. Th...
Clustering is a powerful and efficient technique for pattern recognition which improves classification accuracy. In machine learning, it is a useful unsupervised learning approach due to its simplicity and efficiency for clustering applications. The...
In this paper, a method is presented in order to conduct data classification through the use of a Self-Organizing Map (SOM) Artificial Neural Network (ANN). At first, the performance of the K-means algorithm in relation to the classification of the d...
In order to minimize execution times, improve the quality of solutions, and address more extensive target situations, optimization techniques, particularly metaheuristics, are continually improved. Hybridizing procedures are one of these noteworthy s...
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