M. Premkumar, Garima Sinha, R. Manjula Devi, Santhoshini Sahu, Chithirala Bala Subramanyam, R. Sowmya, Laith Abualigah, Bizuwork Derebew,
Abstract This study presents the K-means clustering-based grey wolf optimizer, a new algorithm intended to improve the optimization capabilities of the conventional grey wolf optimizer in ... optimizer using a new weight factor and the K-means algorithm concepts in order to increase variety and ... Using a partitional clustering-inspired fitness function, the K-means clustering-based grey wolf optimizer was extensively evaluated ... dimensionality. The methodology is based on incorporating the K-means algorithm concept for the purpose of refining initial ...
Tópico(s): Evolutionary Algorithms and Applications
2024 - Nature Portfolio | Scientific Reports
Sanjay Hanji, Savita S. Hanji,
... process is aided by clustering algorithms. Mini Batch K-means (MBK) is an enhanced K-means clustering algorithm which has proved to be efficient ... is to assess the performance of Mini Batch K-means algorithm which is then compared to the standard K-means algorithm using the performance parameters such as quality ... segmentation dataset. The results revealed that Mini batch K-means cluster quality was affected by the number of clusters whereas K-means was not much affected. However, for computational time ...
Tópico(s): Data Mining Algorithms and Applications
2023 - Springer International Publishing | Lecture notes in networks and systems
Yong Li, Xiao Song, Yuchun Tu, Ming Liu,
The differential privacy k-means (DP k-means) clustering algorithm emerged to address the privacy protection challenges in the field of data mining. However, the algorithm encounters difficulties in achieving ... studying privacy budget allocation strategies within the DP k-means clustering algorithm. However, the selection of a privacy budget allocation strategy in the DP k-means algorithm is an NP-hard problem. Our initial ... ensure the convergence and usability of the DP k-means algorithm. Firstly, convergence is ensured by selecting improved ...
Tópico(s): Mobile Crowdsensing and Crowdsourcing
2023 - Elsevier BV | Computers & Security
Islam Zada, Shaukat Ali, Inayat Khan, Myriam Hadjouni, Hela Elmannai, Muhammad Zeeshan, Ali Mohammad Serat, Abid Jameel,
... one of the data extraction methods. The basic K-Mean and Parallel K-Mean partition clustering algorithms work by picking random starting centroids. The basic and K-Mean parallel clustering methods are investigated in this work ... and 5000, respectively. The findings of the Simple K-Mean clustering algorithms alter throughout numerous runs or iterations, ... algorithms separate and identify unique properties of the K-Mean Simple clustering algorithm from the K-Mean Parallel clustering algorithm. Differentiating these features will improve ...
Tópico(s): Advanced Clustering Algorithms Research
2022 - IOS Press | Mobile Information Systems
Yiping Li, Xiangbing Zhou, Jiangang Gu, Ke Guo, Wu Deng,
... In recent years, many scholars have employed the K-Means clustering technique to identify urban hotspots, believing it to be efficient. K-means clustering is a sort of iterative clustering analysis. ... large and the sample size is enormous, the K-Means clustering algorithm is sensitive to the initial clustering ... to obtain better initial clustering centers for the K-Means clustering algorithm. The clustering results are evaluated and ... to the Random Swap clustering algorithm (RS), Genetic K-means algorithm (GAK), Particle Swarm Optimization (PSO) based K- ...
Tópico(s): Human Mobility and Location-Based Analysis
2022 - Multidisciplinary Digital Publishing Institute | Applied Sciences
Mi Li, Eibe Frank, Bernhard Pfahringer,
Abstract The k -means algorithm is widely used for clustering, compressing, and summarizing vector data. We present a fast and memory-efficient GPU-based algorithm for exact k -means, Asynchronous Selective Batched K -means (ASB K -means). Unlike most GPU-based k -means algorithms that require loading the whole dataset onto ... fashion and applies the triangle inequality in each k -means iteration to omit a data point if its ... faster than a GPU-based implementation of standard k -means even in situations when application of the standard ...
Tópico(s): Face and Expression Recognition
2022 - Springer Science+Business Media | Data Mining and Knowledge Discovery
Kristina P. Sinaga, Ishtiaq Hussain, Miin‐Shen Yang,
The k-means algorithm with its extensions is the most used clustering method in the literature. But, the k-means and its various extensions are generally affected by ... of clusters. On the other hand, most of k-means always treat data points with equal importance for feature components. There are several feature-weighted k-means proposed in literature, but, these feature-weighted k-means do not give a feature reduction behavior. In ... entropy-regularized terms we can construct a novel k-means clustering algorithm, called Entropy-k-means, such that ...
Tópico(s): Advanced Data Compression Techniques
2021 - Institute of Electrical and Electronics Engineers | IEEE Access
Lanjun Wan, Gen Zhang, Hongyang Li, Changyun Li,
K-Means clustering algorithm is a typical unsupervised learning method, and it has been widely used in the field of fault diagnosis. However, the traditional serial K-Means clustering algorithm is difficult to efficiently and accurately ... using Spark-based parallel ant colony optimization (ACO)-K-Means clustering algorithm is proposed. Firstly, a Spark-based ... HDFS) and served as the input of ACO-K-Means clustering algorithm. Secondly, ACO-K-Means clustering algorithm suitable for rolling bearing fault diagnosis ...
Tópico(s): Anomaly Detection Techniques and Applications
2021 - Institute of Electrical and Electronics Engineers | IEEE Access
... pre-processing for noise reduction, segmentation using fuzzy k-means clustering and the Zack algorithm [11]. Such enhancement ... filtering technique to the grey image. In addition, k-mean clustering technique has been used to segment the ... the tested image of the blood. The colour k-mean clustering is then applied to the L*a* ... used segmentation technique which is known as colour-k-means clustering. As the authors reported, their thresholding-based proposed segmentation technique outperforms the colour-k-means clustering [21]. Continuously, in 2020, another CAD system ...
Tópico(s): Smart Agriculture and AI
2020 - Institution of Engineering and Technology | IET Image Processing
Kristina P. Sinaga, Miin‐Shen Yang,
The k-means algorithm is generally the most known and used clustering method. There are various extensions of k-means to be proposed in the literature. Although it ... clustering in pattern recognition and machine learning, the k-means algorithm and its extensions are always influenced by ... number of clusters a priori. That is, the k-means algorithm is not exactly an unsupervised clustering method. ... we construct an unsupervised learning schema for the k-means algorithm so that it is free of initializations ...
Tópico(s): Data Mining Algorithms and Applications
2020 - Institute of Electrical and Electronics Engineers | IEEE Access
Pengcheng Guo, Zheng Liu, Jingjing Wang,
... the multi-target ISAR image is segmented using K-means algorithm and then the segmented ISAR image is ... it includes the following factors. First, different from K-means clustering algorithm in [19] which is only suitable ... segmentation methods exploit three clustering methods, i.e. K-means, DBSCAN and PI-DBSCAN, which are operated on ... the radial distance-azimuth distance dimension. In the K-means method, we set the clustering number as the real target number of 2 and employ K-means++ algorithm to initialise other parameters [20]. The parameters ...
Tópico(s): Seismic Imaging and Inversion Techniques
2019 - Institution of Engineering and Technology | IET Radar Sonar & Navigation
Manoj Kumar Gupta, Pravin Chandra,
The k-Means algorithm is extensively used in a number of data clustering applications. In basic k-means, initial cluster centroids are selected on random basis. As a result, every run of k-means leads to the formation of different clusters. Hence, accuracy and performance of k-means is susceptible to the selection of initial cluster ... major role on accuracy and performance of the k-means algorithm. In view of this, a new k-means using Partition based Cluster Initialization method called as 'P-k-means' is proposed in this paper. The experiment is ...
Tópico(s): Face and Expression Recognition
2019 - RELX Group (Netherlands) | SSRN Electronic Journal
Satvik Vats, Bharat Bhushan Sagar,
... of group similar kinds of information. The serial k-means clustering method takes a large amount of computational ... this paper, we evaluated the performance of the K-means algorithm in different ways like K-mean simple (using java codes on MapReduce), K-means (using java codes on MapReduce) with IEC (Initial Equidistant Centres), K-mean on Mahout (using Machine learning library) and K-mean on Spark (Machine learning library) on static IP ... In addition to this we also compare the K-mean simple and K-mean (IEC) on different iteration ...
Tópico(s): Cloud Computing and Resource Management
2019 - Taylor & Francis | Journal of Discrete Mathematical Sciences and Cryptography
Dangguo Shao, Xu Chunrong, Yan Xiang, Peng Gui, Zhu Xiaofang, Chao Zhang, Zhengtao Yu,
... including region growing, the active contour model and k-means technique. The proposed method gets the highest Fm ... as threshold segmentation, region growing, active contour model, k-means and graphics cut. Due to its simple and ... technique in the field of medical image segmentation. K-means is one of the clustering methods [22, 23]. ... compared with the region growing, active contour and k-means. In region growing, the four-neighborhood domain and ... set to 500 for detecting the edge. In k-means method, the image is also grouped into three ...
Tópico(s): Flow Measurement and Analysis
2019 - Institution of Engineering and Technology | IET Image Processing
... solve excessive independence of image segmentation quality of K-means clustering algorithm on initial clustering center for selection, ... image segmentation algorithm, dynamic particle swarm optimization and K-means (DPSOK) based on dynamic particle swarm optimization (DPSO) and K-means clustering was proposed in the Thesis. The performance ... swarm was calculated, and opportunity to transfer to K-means algorithm was found accurately; then K-means clustering center was initialized by utilizing DPSO output ...
Tópico(s): Advanced Computing and Algorithms
2018 - Taylor & Francis | International Journal of Computers and Applications
Ana Elizabeth Marín Celestino, Diego Martínez Cruz, Otazo Sánchez, Francisco Gavi Reyes, David Vásquez Soto,
K-means clustering and principal component analysis (PCA) are widely used in water quality analysis and management. Nevertheless, numerous studies have pointed out that K-means with the squared Euclidean distance is not suitable for high-dimensional datasets. We evaluate a methodology (K-means based on PCA) for water quality evaluation. It ... high dimensional to low for the improvement of K-means clustering. For this, a large dataset of 28 ... wells in the coastal aquifer are classified with K-means clustering for high dimensional and K-means clustering ...
Tópico(s): Water Quality Monitoring and Analysis
2018 - Multidisciplinary Digital Publishing Institute | Water
Muhammad Ali Syakur, Bain Khusnul Khotimah, Eka Mala Sari Rochman, Budi Dwi Satoto,
... batik sales. Researchers will use a combination of K-Means method with elbow to improve efficient and effective k-means performance in processing large amounts of data. K-Means Clustering is a localized optimization method that is ... midpoint of a bad cluster will result in K-Means Clustering algorithm resulting in high errors and poor cluster results. The K-means algorithm has problems in determining the best number ... for the best number of clusters on the K-means method. Based on the results obtained from the ...
Tópico(s): Data Mining and Machine Learning Applications
2018 - IOP Publishing | IOP Conference Series Materials Science and Engineering
Geng Zhang, Chengchang Zhang, Huayu Zhang,
... order to improve the accuracy and stability of K-means algorithm and solve the problem of determining the ... of clusters and best initial seeds, an improved K-means algorithm based on density Canopy is proposed. Firstly, ... Canopy is used as the preprocessing procedure of K-means and its result is used as the cluster number and initial clustering center of K-means algorithm. Finally, the new algorithm is tested on ... samples. The simulation results show that the improved K-means algorithm based on density Canopy achieves better clustering ...
Tópico(s): Data Management and Algorithms
2018 - Elsevier BV | Knowledge-Based Systems
... article, a new initial centroid selection for a K-means document clustering algorithm, namely, Dissimilarity-based Initial Centroid selection for DOCument clustering using K-means (DIC-DOC- K-means), to improve the performance of text document clustering ... comparing the performance of the proposed DIC-DOC- K-means algorithm, the results of the K-means, K-means++ and weighted average of terms-based initial centroid selection + K-means (Weight_Avg_Initials + K-means) clustering algorithms are ...
Tópico(s): Text and Document Classification Technologies
2018 - SAGE Publishing | Journal of Information Science
Abstract The k-means tries to minimize the sum of the squared Euclidean distance from the mean (SSEDM) of each cluster as ... improve quality of the solution produced by the k-means. This approach tries to iteratively improve the quality of solution of the k-means by removing one cluster (minus), dividing another one ( ... again, in each iteration. This method called iterative k-means minus–plus (I-k-means−+). The I-k-means−+ is speeded up using some methods to determine ... clustering process. Results of experiments show that I-k-means−+ can outperform k-means++, to be known one ...
Tópico(s): Complex Network Analysis Techniques
2018 - Elsevier BV | Pattern Recognition
Produção Nacional
Revisado por pares
Emad Taha Khalaf, Muamer N. Mohammad, Kohbalan Moorthy,
... and extract their local features. Further, the scalable K-means++ algorithm is used for partitioning and classification processes, ... for the iris database by using the scalable k-means++ algorithm to divide the data into groups of ... accurate matches. For partitioning-based clustering, the scalable K-means++ (K-means||) algorithm is utilised because of its promising speed ... 3.9.1 Clustering In cluster analysis, the k-means algorithm can be used to divide the input ... each other. In the proposed approach, the scalable K-means++ algorithm is used to find groups in the ...
Tópico(s): Biometric Identification and Security
2018 - Institution of Engineering and Technology | IET Biometrics
Ashutosh Kumar Dubey, Umesh Gupta, Sonal Jain,
... work were: firstly, to compare the performance of k-means and fuzzy c-means (FCM) clustering algorithms; and ... view, the combination of different computational measures for k-means and FCM algorithms for a potential to achieve better clustering accuracy. K-means and FCM algorithms have been considered to understand ... on the breast cancer data. The execution of k-means algorithm is based on centroid, distance, split method, ... same centroid offers better outcome in terms of k-means algorithm. The highest and lowest classification accuracies are ( ...
Tópico(s): Gene expression and cancer classification
2018 - Insight Society | International Journal on Advanced Science Engineering and Information Technology
... The simple traffic recognition is constructed by utilising k-means clustering algorithm to deal with the historical traffic ... of this paper are there aspects. One is k-means algorithm is utilised to deal with the traffic ... collected historical driving cycles data and employing the k-means cluster algorithm. Details are presented as follows. First, ... by utilising the scaling method [4]. Then, the k-means algorithm is utilised to cluster these velocity profiles ... of distance deceleration [%] percent of distance cruising [%] The k-means algorithm [23] is one of the mostly used ...
Tópico(s): Vehicle emissions and performance
2018 - Institution of Engineering and Technology | IET Intelligent Transport Systems
Tanachapong Wangchamhan, Sirapat Chiewchanwattana, Khamron Sunat,
... the selection of the correct data clusters. The k-means algorithm is a well-known method in solving ... a hybrid-clustering algorithm called the hybrid of k-means and Chaotic League Championship Algorithm (KSC-LCA) and ... Furthermore, to overcome the limitation of the original k-means algorithm using the Euclidean distance that cannot handle ... LCA competed with 16 established algorithms including the k-means, k-means++, global k-means algorithms, four search clustering algorithms and nine hybrids of k-means algorithm with several state-of-the-art evolutionary ...
Tópico(s): Data Management and Algorithms
2017 - Elsevier BV | Expert Systems with Applications
Shyr-Shen Yu, Shao-Wei Chu, Chuin-Mu Wang, Yung‐Kuan Chan, Ting-Cheng Chang,
K-means algorithm is the most commonly used simple clustering method. For a large number of high dimensional ... same cluster. In this study, a tri-level k-means algorithm and a bi-layer k-means algorithm are proposed. The k-means algorithm is vulnerable to outliers and noisy data, ... susceptible to initial cluster centers. The tri-level k-means algorithm can overcome these drawbacks. While the data ... paper, an online machine learning based tri-level k-means algorithm is also provided to solve this problem. ...
Tópico(s): Metaheuristic Optimization Algorithms Research
2017 - Elsevier BV | Applied Soft Computing
Saeed Hasanvand, Majid Nayeripour, Seyed Ali Arefifar, Hossein Fallahzadeh‐Abarghouei,
... as frequency and duration of interruptions. Then, the k -means algorithm, based on weighted graph partitioning method, is ... modified particle swarm optimisation (PSO) algorithm. Moreover, the k -means and Silhouette global coefficient algorithms are used to ... of MGs. Proposing a robust approach based on k -means method to cluster the distribution systems. Using Silhouette ... 3 presents graph theory and partitioning procedure using k -means method. In Section 4, the modelling of system ... standard clustering algorithm, which uses Euclidean space named k -means method. The optimal number of clusters is then ...
Tópico(s): Power Systems and Technologies
2017 - Institution of Engineering and Technology | IET Generation Transmission & Distribution
Hongjie Wu, Haiou Li, Min Jiang, Cheng Chen, Qiang Lv, Chuang Wu,
... population increases. Results. Here, we proposed two enhanced K -means clustering algorithms capable of robustly identifying high-quality ... SPICKER to determine the initial centroids for basic K -means clustering ( S K -means), whereas the other employs squared distance to optimize the initial centroids ( K -means++). Our results showed that S K -means and K -means++ were more robust as compared with SPICKER alone, ... of SPICKER. Conclusions. We observed that the classic K -means algorithm showed a similar performance to that of ...
Tópico(s): Bioinformatics and Genomic Networks
2017 - Hindawi Publishing Corporation | BioMed Research International
The global k-means algorithm is an incremental approach to clustering that dynamically adds one cluster center at a time through a ... search procedure from suitable initial positions, and employs k-means to minimize the sum of the intra-cluster variances. However the global k-means algorithm sometimes results singleton clusters and the initial ... poor local optimal can be easily obtained by k-means algorithm. In this paper, we modified the global k-means algorithm to eliminate the singleton clusters at first, ...
Tópico(s): Data Management and Algorithms
2016 - Springer International Publishing | SpringerPlus
Bashar Aubaidan, Masnizah Mohd, Mohammed Albared,
... study of two document clustering techniques which are k-means and k-means++. In particular, we compare the two main approaches in crime document clustering. The drawback of k-means is that the user needs to define the ... a trivial task. To overcome this problem, a k-means++ was introduced in order to find a good initial center point. Since k-means++ has not being applied before in crime document clustering, this study presented a comparative study between k-means and k-means++ to investigate whether the initialization ...
Tópico(s): Data Mining Algorithms and Applications
2014 - Science Publications | Journal of Computer Science
María Luz López García, Ricardo Garćıa-Ródenas, A. González Gómez,
... dimensional nature. In this paper the use of K-means algorithms to solve this problem is analysed. A comparative study of three K-means algorithms has been conducted. The K-means algorithm for raw data, a kernel K-means algorithm for raw data and a K-means algorithm using two distances for functional data are ... of the sampling {xi}i=1n on the K-means algorithm performance. In the numerical study an ex ... sampling is not uniform in X, then a K-means algorithm that ignores the functional nature of the ... performance. It is numerically shown that the original K-means algorithm and that suggested here lead to similar ...
Tópico(s): Advanced Clustering Algorithms Research
2014 - Elsevier BV | Neurocomputing