Shape similarity differential privacy trajectory protection mechanism based on relative entropy and K-means by Suxia ZHU, Shulun LIU, Guanglu SUN

“…To solve the problem that most studies had not fully considered the sensitivity of location to privacy budget and the influence of trajectory shape, which made the usability of published trajectory poor, a shape similarity differential privacy trajectory protection mechanism based on relative entropy and K-means was proposed.Firstly, according to the topological relationship of geographic space, relative entropy was used to calculate the sensitivity of real location to privacy budget, a real-time calculation method of location sensitive privacy level was designed, and a new privacy model was built in combination with differential privacy budget.Secondly, K-means algorithm was used to cluster the release position to obtain the release position set that was most similar to the real position direction, and Fréchet distance was introduced to measure the similarity between the release track and the real track, so as to improve the availability of the release track.Experiments on real data sets show that the proposed trajectory protection mechanism has obvious advantages in trajectory availability compared with others.…”
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Application of Intelligent Fuzzy Decision Tree Algorithm in English Teaching Model Improvement by Jingjing Li

“…In this paper, we propose a way to obtain the centroids of continuous attribute clustering by K-means algorithm and combine the triangular fuzzy number to fuzzy the continuous data. …”
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PERFORMANCE ANALYSIS OF LSTM MODEL WITH MULTI-STEP AHEAD STRATEGIES FOR A SHORT-TERM TRAFFIC FLOW PREDICTION by Erdem DOĞAN

“…In addition, databases are clustered using the k-means++ algorithm to reduce the number of experiments. …”
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Service clustering method based on description context feature words and improved GSDMM model by Qiang HU, Jiaji SHEN, Guanghui JING, Junwei DU

“…To address the problem that current service clustering methods usually faced low quality of service representation vectors, a service clustering method based on description context feature words and improved GSDMM model was proposed.Firstly, a feature word extraction method based on context weight was constructed.The words that fit well with the context of service description were extracted as the set of feature words for each service.Then, an improved GSDMM model with topic distribution probability correction factor was established to generate service representation vectors and achieve distribution probability correction for non-critical topic items.Finally, K-means++ algorithm was employed to cluster Web services based on these service representation vectors.Experiments were conducted on real Web services in Web site of Programmable Web.Experiment results show that the quality of service representation vectors generated by the proposed method is higher than of other topic models.Further, the performance of our clustering method is significantly better than other service clustering methods.…”
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Optimasi Proses Klasterisasi di MySQL DBMS dengan Mengintegrasikan Algoritme MIC-Kmeans Menggunakan Bahasa SQL dalam Stored Procedure by Issa Arwani

“…However, it carries the weakness of the K-means algorithm itself in the duration of iterations to reach convergence and the accuracy of clustering due to the centroid initialization process randomly. …”
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Financial Stability and Innovation: The Role of Non-Performing Loans by Massimo Arnone, Alberto Costantiello, Angelo Leogrande, Syed Kafait Hussain Naqvi, Cosimo Magazzino

“…The data were obtained from the World Bank and the Global Innovation Index over the period 2013–2022 for 149 countries. The k-means algorithm was used to verify the presence of clusters in the data. …”
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A Novel Clustering Algorithm Inspired by Membrane Computing by Hong Peng, Xiaohui Luo, Zhisheng Gao, Jun Wang, Zheng Pei

“…Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature.…”
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Analyzing Students' Interest in Mathematics Through the Implementation of the K-Means Clustering Algorithm by Dede Wintana, Hamdun Sulaiman, Ramdhan Saepul Rohman, Gunawan Gunawan, Muhammad Abdul Ghani

“…This study aims to identify the level of student interest in mathematics using the K-Means algorithm. This method is used to group students into several clusters based on their level of interest. …”
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Finding Possible Precursors for the 2015 Cotopaxi Volcano Eruption Using Unsupervised Machine Learning Techniques by Juan C. Anzieta, Hugo D. Ortiz, Gabriela L. Arias, Mario C. Ruiz

“…To find these families we applied a two-stage process in which the events were first separated by their frequency content by applying the k-means algorithm to the spectral density vector of the signals and then were further separated by their waveform by applying Correntropy and Dynamic Time Warping. …”
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Extending emotional lexicon for improving the classification accuracy of Chinese film reviews by Qiaoyun Wang, Guangli Zhu, Shunxiang Zhang, Kuan-Ching Li, Xiang Chen, Hanqing Xu

“…First, using the improved K-means++ algorithm to cluster and select seed words with obvious emotional tendencies. …”
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A Case Study on Monolith to Microservices Decomposition with Variational Autoencoder-Based Graph Neural Network by Rokin Maharjan, Korn Sooksatra, Tomas Cerny, Yudeep Rajbhandari, Sakshi Shrestha

“…Then, a variational autoencoder (VAE) is used to extract features from the components of a monolithic application. Finally, the C-means algorithm is used to cluster the components into possible microservices. …”
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Clustering Batik SMEs: Open Innovation for Environmental Sustainability by Amelia Kurniawati, Fahmy Habib Hasanudin, Fandi Achmad, Raihan Abdurrahman, Rizki Fajar Ahmad Gurnita

“…The clustering process is performed using the K-Means algorithm. The results show that the data are grouped into two clusters. …”
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A Hybrid Enhancement Artificial Bee Colony Algorithm for High-efficiency Service Selection by ZHANG Hong-guo, CHEN Yang, MA Chao, FANG Zhou, HUANG Hai

“…The algorithm combines K-means algorithm，K-Nearest Neighbor ( KNN) algorithm and ABC algorithm to ensure that ABC algorithm always maintains continuity when updating solutions in discrete solution space. …”
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Optimizing Zakat Distribution with GIS and Data Mining in Community Empowerment at BAZNAS Deli Serdang by Sinta Dara Efita, Triase Triase

“…Tanjung Morawa was identified as the district with the highest number of zakat recipients, namely 239 people, which shows a significant need. The K-Means algorithm plays an important role in identifying areas that need help, so this Geographic Information System-based grouping is proven to improve the efficiency of zakat resource allocation. …”
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WSN clustering routing algorithm based on Cuckoo Search algorithm optimized K-means by Kailei ZHU, Aijing SUN

“…In order to extend the lifetime of wireless sensor network (WSN), a clustering routing algorithm for WSN based on Cuckoo Search (CS) algorithm optimized K-means was presented.In the clustering stage, the initial cluster centers were selected by CS algorithm, which make the clustering results of the K-means algorithm more uniform to balance node energy consumption.The remaining energy of the node, the distance from the center of the cluster were comprehensively considered in the cluster election, and the weight according to the remaining energy of the node was dynamically adjusted.In the data communication stage, in order to further balance the load of the cluster head, the remaining energy of the relay node and its load, and the cluster head routing energy consumption were comprehensively considered, CS algorithm was combined to plan routing for the cluster head.The simulation results show that the proposed algorithm is better than LEACH-K, LEACH-improve and DTK-means in terms of energy consumption balance.With the death of the first node as the life cycle of the network, the network lifespan was increased by 173%, 21%, and 6% respectively.The proposed algorithm effectively extending the network life cycle.…”
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Intelligent Analysis of Logistics Information Based on Dynamic Network Data by Pengbo Yang

“…This paper builds an experimental environment based on Hadoop and MapReduce parallelization based on K-means algorithm. Taking the obtained logistics data as the analysis object, preprocess it and get the results based on cloud clustering mining. …”
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Measuring customer loyalty using an extended RFM and clustering technique by Zohre Zalaghi, Yousef Abbasnejad Varzi

“…In this paper, a method is introduced that obtains the behavioral traits of customers using the extended RFM approach and having the information related to the customers of an organization; it then classifies the customers using the K-means algorithm and finally scores the customers in terms of their loyalty in each cluster. …”
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Technique for target recognition based on intuitionistic fuzzy c-means clustering and kernel matching pursuit by Yang LEI, Wei-wei KONG, Ying-jie LEI

“…Kernel matching pursuit requires every step of searching process be global optimal searching in the redundant dictionary of function.Namely,the dictionary learning time of KMP was too long.To the above drawbacks,a novel technique for KMP based on IFCM was proposed to substitute local searching for global searching by the property superiority of dynamic clustering performance,which was also the superiority in Intuitionistic fuzzy c-means algorithm.Then two testing including classification and effectiveness were carried out towards four real sample data.Subsequently,high resolution range profile (HRRP)was selected from the classical properties of target recognition in e middle ballistic trajectory,which were extracted for getting sub-range profile.Finally,three algorithms including FCM,KMP,IFCM-KMP were carried out respectively towards different kinds of sub-range profile samples in emulation platform,the conclusion of which fully demonstrates that the IFCM-KMP algorithm is superior over FCM and KMP when it comes to target recognition in the middle ballistic trajectory.…”
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Edge Detection in UAV Remote Sensing Images Using the Method Integrating Zernike Moments with Clustering Algorithms by Liang Huang, Xueqin Yu, Xiaoqing Zuo

“…To begin with, two typical clustering algorithms, namely, fuzzy c-means (FCM) and K-means algorithms, are used to cluster the original remote sensing images so as to form homogeneous regions in ground objects. …”
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Face Detection Method based on Lightweight Network and Weak Semantic Segmentation Attention Mechanism by Xiaoyan Wu

“…A face detection method based on lightweight network and weak semantic segmentation attention mechanism is proposed in this paper, aiming at the problems of low detection accuracy and slow detection speed in face detection in complex scenes. K-means++ algorithm is employed to perform clustering analysis on YOLOv4 model prior frames in this paper, and smaller size prior frames are set to capture small face information to solve the missing detection problem of small face targets in scenes. …”
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