Improved K-means Clustering Algorithm Research Articles

Analysing social media with an Improved K-Means Clustering Algorithm

Nowadays, sharing human social behavior and growing a multi-user network requires the use of social media. Analysis of social media provides a valuable opportunity to study human social activity at scale. People often bring and talk about different kind of topics on social media platforms. depending on which, discussions are made to show the positive as well dis positives ways. This is an interesting point about using social media. There is a great deal of information about the type and substance of these discussion holography’s that informs us more broadly around patterns in social media interactions, and how content flows between individuals. Data mining techniques exists which can be utilized to crunch out user information from social media and relationships existing within the network. However, state of the art methods often fail to model user communities and their behaviors at a correct level. To solve this problem and help successfully do the grouping of related information, in our work here we present an enhanced fuzzy means clustering approach. Using this technique reduces the creation and time complexity of clusters, resulting in higher quality group outcomes. Our proposed system aims at mitigating the problems as described above by introducing a scalable and effective solution for real-time cluster social media data.

Analysing social media with an Improved K-Means Clustering Algorithm

Nowadays, sharing human social behavior and growing a multi-user network requires the use of social media. Analysis of social media provides a valuable opportunity to study human social activity at scale. People often bring and talk about different kind of topics on social media platforms. depending on which, discussions are made to show the positive as well dis positives ways. This is an interesting point about using social media. There is a great deal of information about the type and substance of these discussion holography’s that informs us more broadly around patterns in social media interactions, and how content flows between individuals. Data mining techniques exists which can be utilized to crunch out user information from social media and relationships existing within the network. However, state of the art methods often fail to model user communities and their behaviors at a correct level. To solve this problem and help successfully do the grouping of related information, in our work here we present an enhanced fuzzy means clustering approach. Using this technique reduces the creation and time complexity of clusters, resulting in higher quality group outcomes. Our proposed system aims at mitigating the problems as described above by introducing a scalable and effective solution for real-time cluster social media data.

An Improved K-means Clustering Algorithm Towards an Efficient Educational and Economical Data Modeling

An Improved K-means Clustering Algorithm Towards an Efficient Educational and Economical Data Modeling

An Efficient Division Method of Traffic Cell Based on Improved K-means Clustering Algorithm for the Location of Infrastructure in Vehicular Networks

An Efficient Division Method of Traffic Cell Based on Improved K-means Clustering Algorithm for the Location of Infrastructure in Vehicular Networks

Retracted: Digital Industry Financial Risk Early Warning System Based on Improved K-Means Clustering Algorithm.

[This retracts the article DOI: 10.1155/2022/6797185.].

Retracted: Research on Reconstruction of Basketball Training Action Trajectory Based on Improved K-Means Clustering Algorithm

Retracted: Research on Reconstruction of Basketball Training Action Trajectory Based on Improved K-Means Clustering Algorithm

An Optimal Scheduling Method for an Integrated Energy System Based on an Improved k-Means Clustering Algorithm

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-means combined with box plots (Imk-means) to improve the convergence speed of the heuristic algorithm by forming its initial conditions. Thus, the optimization scheduling speed is enhanced. First of all, considering the system source and load factors, the Imk-means is presented to find the typical and extreme days in a historical optimization dataset. The output results for these typical and extreme days can represent common and abnormal optimization results, respectively. Thus, based on the representative historical data, a traditional heuristic algorithm with an initial solution set, such as the genetic algorithm, can be accelerated greatly. Secondly, the initial populations of the genetic algorithm are dispersed at the historical outputs of the typical and extreme days, and many random populations are supplemented simultaneously. Finally, the improved genetic algorithm performs the solution process faster to find optimal results and can possibly prevent the results from falling into local optima. A case study was conducted to verify the effectiveness of the proposed method. The results show that the proposed method can decrease the running time by up to 89.29% at the most, and 72.68% on average, compared with the traditional genetic algorithm. Meanwhile, the proposed method has a slightly increased optimization index, indicating no loss of optimization accuracy during acceleration. It can also indicate that the proposed method does not fall into local optima, as it has fewer iterations.

Construction of Driving Condition Based on Discrete Fourier Transform and Improved K-Means Clustering Algorithm

Abstract In view of the low execution efficiency and slow convergence speed of traditional clustering algorithms, the initial clustering center has a greater impact on the clustering results, which leads to the problem of reduced algorithm accuracy. This paper proposes an improved K-means algorithm (Grid-K-means), that is, the Grid density is used to determine the initial clustering center; According to the density, the grid points are sorted to eliminate the idea of noise grid points and invalid grid points, so as to improve the efficiency and accuracy of the algorithm. First, the discrete Fourier transform was used to filter the original data, and then the principal component analysis and the improved K-means clustering algorithm were used to reduce and classify the kinematics fragments respectively, so as to construct the driving conditions of the vehicle. The experimental results show that this method can effectively improve the construction accuracy and reduce the construction time, and the fitted driving conditions can effectively reflect the local actual traffic conditions.

Construction of Diesel Vehicle Driving Condition Based on Improved K-Means Clustering Algorithm

Construction of Diesel Vehicle Driving Condition Based on Improved K-Means Clustering Algorithm

Design and Implementation of an Improved K-Means Clustering Algorithm

Aiming at the problems of the traditional K-means clustering algorithm, such as the local optimal solution and the slow clustering speed caused by the uncertainty of k value and the randomness of the initial cluster center selection, this paper proposes an improved KMeans clustering method. The algorithm first uses the idea of the elbow rule based on the sum of squares of errors to obtain the appropriate number of clusters k, then uses the variance as a measure of the degree of dispersion of the samples, and selects k data points with the smallest variance and the distance greater than the average distance of the samples as the initial clustering center of the data set. Finally, combined with the “triangular inequality principle,” the unnecessary distance calculation of the KMeans algorithm in the iterative process is reduced, and the algorithm operation efficiency is improved. The results show that the improved KMeans clustering algorithm is tested on the UCI data set. Compared with the traditional k-means algorithm and Canopy-KMeans algorithm, the accuracy and speedup ratio of the improved KMeans clustering algorithm are significantly improved, and the clustering quality is improved.

Digital Industry Financial Risk Early Warning System Based on Improved K-Means Clustering Algorithm.

Corporate financial risks not only endanger the financial stability of digital industry but also cause huge losses to the macro-economy and social wealth. In order to detect and warn digital industry financial risks in time, this paper proposes an early warning system of digital industry financial risks based on improved K-means clustering algorithm. Aiming to speed up the K-means calculation and find the optimal clustering subspace, a specific transformation matrix is used to project the data. The feature space is divided into clustering space and noise space. The former contains all spatial structure information; the latter does not contain any information. Each iteration of K-means is carried out in the clustering space, and the effect of dimensionality screening is achieved in the iteration process. At the same time, the retained dimensions are fed back to the next iteration. The dimensional information of the cluster space is discovered automatically, so no additional parameters are introduced. Experimental results show that the accuracy of the proposed algorithm is higher than other algorithms in financial risk detection.

Research on Reconstruction of Basketball Training Action Trajectory Based on Improved K-Means Clustering Algorithm

The automatic capture and analysis of basketball game movements can guide basketball training and provide an effective method for improving the efficiency of basketball training. This paper introduces the research status of clustering methods in the field of trajectory data mining and reconstruction in detail. By analyzing the trajectory data under the constraints of the road network, the spatiotemporal characteristics of the existing trajectory clustering methods, and the deficiencies of the existing trajectory clustering methods, a new trajectory clustering method based on trajectory segmentation and spatiotemporal similarity measurement is implemented. A motion capture and reconstruction method for basketball training based on visual image K-means clustering algorithm is proposed. Multiresolution frame scanning technology is used to collect machine images of basketball training movements, and edge contour processing is performed on the collected high-resolution basketball training movement images. Feature detection uses the three-dimensional model reconstruction method to segment the basketball training action area and combines the irregular triangle network model to realize the machine vision block template matching processing of basketball training actions and capture the basketball training action in the Gaussian fuzzy affine space. In time and feature extraction, wavelet lifting technology is used to identify the ambiguity of basketball training movements, image enhancement technology is used to improve the resolution and adaptability of basketball training movement capture, and machine vision image processing methods are used to achieve basketball training movement capture optimization. The simulation results show that the method has better adaptability and higher feature recognition ability for basketball training motion capture and improves the feature extraction and adaptive capture reconstruction ability of basketball training motion.

Analysis of Learning Ability of Ideological and Political Course Based on BP Neural Network and Improved k -Means Cluster Algorithm

With the rapid development of technologies such as big data analysis, machine learning, and cloud computing, artificial intelligence has made breakthrough progress in many fields. Artificial intelligence technology has also brought profound changes to higher education. Therefore, the ideological and political course in colleges and universities should integrate artificial intelligence technology into the teaching of ideological and political education and create an “intelligent ideological and political learning” to adapt to the goal of educational reform in the new era. This paper presents a research method of innovation ability of ideological and political course based on BP neural network and improved k -means clustering algorithm. Firstly, this method obtains the objective index that can comprehensively measure the learning ability through BP neural network and acquires the evaluation score of learning ability. Then, SPSS software is utilized to test the correlation between the influencing factors and the index, harvesting the factors that significantly affect graduate students’ ideological and political learning ability. Finally, an improved k -means clustering algorithm is designed, which clusters the graduate students according to the different characteristics of the survey objects and gives targeted suggestions for each class of individuals to improve their ideological and political learning ability. The experimental results indicate that the proposed method is feasible and effective. The research method of ideological and political course ability proposed in this paper is of great significance to the promotion of ideological and political education in the era of big data.

Improved k-Means Clustering Algorithm for Big Data Based on Distributed SmartphoneNeural Engine Processor

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 clustering time. Therefore, many techniques have been implemented to improve the performance of the clustering algorithms, especially for k-means clustering. In this paper, the neural-processor-based k-means clustering technique is proposed to cluster big data by accumulating the advantage of dedicated machine learning processors of mobile devices. The solution was designed to be run with a single-instruction machine processor that exists in the mobile device’s processor. Running the k-means clustering in a distributed scheme run based on mobile machine learning efficiently can handle the big data clustering over the network. The results showed that using a neural engine processor on a mobile smartphone device can maximize the speed of the clustering algorithm, which shows an improvement in the performance of the cluttering up to two-times faster compared with traditional laptop/desktop processors. Furthermore, the number of iterations that are required to obtain (k) clusters was improved up to two-times faster than parallel and distributed k-means.

Research on Driving Conditions and Fuel Consumption of Improved K-means Clustering Algorithm

Abstract In order to solve the problem that the initial center of traditional clustering algorithm is easy to fall into local optimum and time-consuming. An improved combination optimization algorithm of principal component analysis and weighted K-means clustering is proposed. The algorithm introduces the maximum and minimum distance, weighted Euclidean distance, starting from the mean sum of the distances of the remaining clustering points, avoiding the influence of outliers and edge data. The proportion method is used to improve the principal component, and the characteristic influence factor obtained is used as the initial characteristic weight to construct a weighted Euclidean distance metric. According to the influence factors of feature contribution rate on clustering, a clustering method of feature weight influence factors is proposed. The representative feature factors are selected to highlight the clustering effect. Finally, the driving cycle of automobile is synthesized and the instantaneous fuel consumption is analyzed. The results show that: the difference value of speed acceleration joint distribution of the proposed method is only 1.05%, which saves 44.2% of the time compared with the traditional K-means clustering, and the driving cycle fitting degree is high, which can reflect the actual vehicle operation characteristics and fuel consumption.

The Classification System of Literary Works Based on K-Means Clustering

The plots in certain literary works are very complicated and hinder readers from understanding them. Therefore tools should be proposed to support readers; comprehension of complex literary works supports their understanding by providing the most important information to readers. A human reader must capture multiple levels of abstraction and meaning to formulate an understanding of a document. Hence, in this paper, an Improved K-means clustering algorithm (IKCA) has been proposed for literary word classification. For text data, the words that can express exact semantic in a class are generally better features. This paper uses the proposed technique to capture numerous cluster centroids for every class and then select the high-frequency words in centroids the text features for classification. Furthermore, neural networks have been used to classify text documents and k-mean to cluster text documents. To develop the model based on unsupervised and supervised techniques to meet and identify the similarity between documents. The numerical results show that the suggested model will enhance to increases quality comparison of the existing Algorithm and K-means algorithm, accuracy comparison of ALA and IKCA (95.2%), time is taken for clustering is less than 2 hours, success rate (97.4%) and performance ratio (98.1%).

A New Frequency Hopping Signal Detection of Civil UAV Based on Improved K-Means Clustering Algorithm

The rapid development of civil UAV promotes the social and economic development, and the frequent “flying illegally” events has brought great challenges to aviation safety and government supervision. The frequency hopping communication system used in UAV data transmission and control link has the advantages of anti-jamming and anti-interception, and its complex electromagnetic environment, which also brings great difficulties to UAV detection. In this paper, the detection of civil UAV is realized by frequency hopping signal monitoring. Firstly, by analyzing the signal characteristics of UAVs, an adaptive noise threshold calculation method is proposed for find the signals from spectrum data. Then, the improved clustering analysis algorithm is proposed based on constructed the waveform shape characteristics and peak characteristics of UAV frequency hopping signal. Finally, according to the designed experimental process, the experimental environment is set up, and the UAV monitoring, discovery and parameter estimation are realized by using the improved clustering analysis algorithm, and compared with K-means, K-means++, DBSCAN, Multi-hop and Auto-correlation methods. The results show that the method has certain robustness and has a good application prospect.

State Recognition of Mill Load Based on Improved K-means Clustering Algorithm

In this work, a new identification method for the load identification of ball mill based on improved K-means clustering is proposed. Since the external device response signals of ball mill are closely related to its load during the operation, the appropriate parameters are selected. The number of clusters and initial clustering centers are meliorated based on their density and genetic algorithm. The dimensionality of input features are reduced with kernel principal component analysis (KPCA), and the final clustering centers are obtained by iterating in the space with reduced dimensionalities. Tests on datasets have demonstrated the higher accuracy and stability of our improved K-means method compared with other methods. The case-analysis results show that the mill load status can be identified clearly according to the displacement variations of cluster centers calculated by this method. Our research provides a new reference for mill load identification in the viewpoint of multi-source information fusion.

Improved K-Means Clustering Algorithm Based on Dynamic Clustering

Improved K-Means Clustering Algorithm Based on Dynamic Clustering

Improved K-means Clustering Algorithm based on Dynamic Clustering

Improved K-means Clustering Algorithm based on Dynamic Clustering