Relevant bibliographies by topics / K-Mode Clustering

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Academic literature on the topic 'K-Mode Clustering'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "K-Mode Clustering"

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Kaur, Manmeet, and Amrit Kaur. "An Algorithm to Mitigate the Attacker by Applying Homomorphic Encryption." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 7 (2017): 254. http://dx.doi.org/10.23956/ijarcsse/v7i7/0121.

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K-Modes is an eminent algorithm for clustering data set with categorical attributes. This algorithm is famous for its simplicity and speed. The KModes is an extension of the K-Means algorithm for categorical data. Since K-Modes is used for categorical data so ‘Simple Matching Dissimilarity’ measure is used instead of Euclidean distance and the ‘Modes’ of clusters are used instead of ‘Means’.The major drawback of k-mode is that the user needs to define the centroid points. To overcome this problem, k-mode with entropy based similarity coefficient was introduced in order to find good initial cen
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Li, Wenwei, Xiaoming Wang, and Jun Shi. "Smart Grid Demand-side Response Model Based on Fuzzy Clustering Analysis." Journal of Physics: Conference Series 2355, no. 1 (2022): 012059. http://dx.doi.org/10.1088/1742-6596/2355/1/012059.

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Abstract Grid demand-side response forecasting is greatly affected by new energy generation power. Wind power generation and time are irrelevant. Wind power generation brings greater challenges to the grid, so it is necessary to study the problem of wind power generation. An improved fuzzy clustering demand-side response model based on K-means mode division is proposed, in which the number of wind speed modes of the day to be forecasted is determined according to the K-means mode division method. The historical wind speed data with high correlation coefficients are screened out by fuzzy cluste
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Goyal, Akarsh, Patra Anupam Sourav, and P. Kalyanaraman. "Application of Genetic Algorithm Based Intuitionistic Fuzzy k-Mode for Clustering Categorical Data." Cybernetics and Information Technologies 17, no. 4 (2017): 99–113. http://dx.doi.org/10.1515/cait-2017-0044.

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AbstractIn present times a great number of clustering algorithms are available which group objects having similar features. But most of the datasets have data values that are categorical, which makes it difficult to implement these algorithms. The concept of genetic algorithm on intuitionistic fuzzy k-Mode method is proposed in the paper to cluster categorical data. This model is an extension of intuitionistic fuzzy k-Mode in which the notion of fitness related objective functions, crossovers, mutations and probability has been added to provide better clusters for the data objects. Also the in
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Zhu, Li, Wenhao Liu, Rongdi Zhang, and Bingjie Dong. "Credit Risk Evaluation of Supply Chain Finance Based on K-Means-SVM Model." Advances in Engineering Technology Research 1, no. 2 (2022): 221. http://dx.doi.org/10.56028/aetr.1.2.221.

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With the rapid development of supply chain finance, it is important to evaluate its credit risk effectively. The Support Vector Machine (SVM) is designed to construct the credit risk measurement model of supply chain finance. Considering the characteristics of SVM model, we select the clustering center based on K-Means clustering algorithm and the edge points far from the clustering center as training samples to train the SVM model. Experimental results show that compared with single SVM model, the overall classification accuracy of K-means-SVM model is increased by 7.2%, and the first type er
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Zhu, Li, Wenhao Liu, Rongdi Zhang, and Bingjie Dong. "Credit Risk Evaluation of Supply Chain Finance Based on K-Means-SVM Model." Advances in Engineering Technology Research 2, no. 1 (2022): 221. http://dx.doi.org/10.56028/aetr.2.1.221.

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With the rapid development of supply chain finance, it is important to evaluate its credit risk effectively. The Support Vector Machine (SVM) is designed to construct the credit risk measurement model of supply chain finance. Considering the characteristics of SVM model, we select the clustering center based on K-Means clustering algorithm and the edge points far from the clustering center as training samples to train the SVM model. Experimental results show that compared with single SVM model, the overall classification accuracy of K-means-SVM model is increased by 7.2%, and the first type er
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Ding, Ting, and Mengqi Zhang. "Business English Teaching Reform Under the Background of Artificial Intelligence + Big Data." International Journal of Web-Based Learning and Teaching Technologies 19, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijwltt.350269.

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The level of information technology is increasing, and technology is developed. University English teaching has also changed under its influence. Different from the traditional teaching in the past, more and more students adopt the mode of “Internet + Smartphone” to learn English. This paper proposes a teaching mode evaluation method in the context of big data. Through the K-means algorithm based on it, the data clustering of business English teaching mode is completed. According to the data obtained from clustering, combined with the standardization of positive and negative indicators, the bu
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Pires, A. C. B., and N. Harthill. "Statistical analysis of airborne gamma‐ray data for geologic mapping purposes: Crixas‐Itapaci area, Goias, Brazil." GEOPHYSICS 54, no. 10 (1989): 1326–32. http://dx.doi.org/10.1190/1.1442592.

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Q‐mode factor analysis, K‐means clustering, and G‐mode clustering were used on digitized gamma‐ray spectrometer data from an aerial survey of the Crixas‐Itapaci area, Goias, Brazil. The data points including seven variables—eU, eTh, K, total count, U/Th, U/K, and Th/K—were digitized for a 2 km square grid. For the northwest corner of the area the data were gridded at 1 km. The Q‐mode classification method supplied results that do not show a good correspondence with the known geology. The K‐means clustering procedure barely identified the main lithologic features of the area. The G‐mode techniq
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Guo, Lina. "Optimization of Regional Industrial Structure Based on Multiobjective Optimization and Fuzzy Set." Mathematical Problems in Engineering 2022 (June 24, 2022): 1–10. http://dx.doi.org/10.1155/2022/4006967.

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With the development of China’s economy, it is required to change the mode of economic growth, from extensive growth mode to intensive growth mode. Therefore, the research on industrial structure is of great significance. This study proposes a regional industrial structure optimization method based on multiobjective optimization and fuzzy set. Firstly, the multiobjective industrial structure evaluation model is constructed, then the evaluation model based on improved fuzzy industrial structure is proposed, and finally the application effect of improved fuzzy industrial structure evaluation mod
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Zhang, Bei, Luquan Wang, and Yuanyuan Li. "Precision Marketing Method of E-Commerce Platform Based on Clustering Algorithm." Complexity 2021 (March 5, 2021): 1–10. http://dx.doi.org/10.1155/2021/5538677.

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In user cluster analysis, users with the same or similar behavior characteristics are divided into the same group by iterative update clustering, and the core and larger user groups are detected. In this paper, we present the formulation and data mining of the correlation rules based on the clustering algorithm through the definition and procedure of the algorithm. In addition, based on the idea of the K-mode clustering algorithm, this paper proposes a clustering method combining related rules with multivalued discrete features (MDF). In this paper, we construct a method to calculate the simil
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Zhou, Jixiang, Ruixing Wang, Weijian Huang, Zhong Zhao, Fengfeng Zheng, and Zhiyun Wang. "Location and Layout of Electric Vehicle Charging Stations Based on K-Means Algorithm." Journal of Physics: Conference Series 2592, no. 1 (2023): 012070. http://dx.doi.org/10.1088/1742-6596/2592/1/012070.

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Abstract The development of the electric vehicle industry can be promoted by the reasonable layout of electric vehicle charging stations. In this study, the construction of charging piles for new energy vehicles in Guangzhou was discussed. Specifically, the location of the charging pile clustering center was selected using the K-Means clustering algorithm. The K-Means clustering results were analyzed through the elbow method to solve the optimal construction partition of charging piles. Simulation modeling revealed that when k=6, charging piles could be used more conveniently when being about
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Dissertations / Theses on the topic "K-Mode Clustering"

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Kondur, Navyaram Venkata. "Using K-Mode Clustering to Identify Personas for Technology on the Trail." Thesis, Virginia Tech, 2018. http://hdl.handle.net/10919/83466.

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Hiking is a widely-used term used differently by different people, and technology has an increasing role in the hikes that people take. Given the tremendous growth in technology capabilities for fitness, navigation, and communication, the breadth of devices and applications has expanded. Use of technology differs based on not only individuals but also the kinds of tasks performed. This research seeks to understand the different perspectives of the hikers and the technology they carry with them on the trail through a survey, analysis, and persona creation. 40 self-described hikers participated
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Zhang, Pei. "Beam position diagnostics with higher order modes in third harmonic superconducting accelerating cavities." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/beam-position-diagnostics-with-higher-order-modes-in-third-harmonic-superconducting-accelerating-cavities(587aa24b-8adc-4bc6-8f5c-475aa0028d06).html.

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Higher order modes (HOM) are electromagnetic resonant fields. They can be excited by an electron beam entering an accelerating cavity, and constitute a component of the wakefield. This wakefield has the potential to dilute the beam quality and, in the worst case, result in a beam-break-up instability. It is therefore important to ensure that these fields are well suppressed by extracting energy through special couplers. In addition, the effect of the transverse wakefield can be reduced by aligning the beam on the cavity axis. This is due to their strength depending on the transverse offset of
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Bjarnason, Brynjar Smári. "Clustering metagenome contigs using coverage with CONCOCT." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-208944.

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Metagenomics allows studying genetic potentials of microorganisms without prior cultivation. Since metagenome assembly results in fragmented genomes, a key challenge is to cluster the genome fragments (contigs) into more or less complete genomes. The goal of this project was to investigate how well CONCOCT bins assembled contigs into taxonomically relevant clusters using the abundance profiles of the contigs over multiple samples. This was done by studying the effects of different parameter settings for CONCOCT on the clustering results when clustering metagenome contigs from in silico model c
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Maharjan, Nadim, and Paria Moazzemi. "Telemetry Network Intrusion Detection System." International Foundation for Telemetering, 2012. http://hdl.handle.net/10150/581632.

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ITC/USA 2012 Conference Proceedings / The Forty-Eighth Annual International Telemetering Conference and Technical Exhibition / October 22-25, 2012 / Town and Country Resort & Convention Center, San Diego, California<br>Telemetry systems are migrating from links to networks. Security solutions that simply encrypt radio links no longer protect the network of Test Articles or the networks that support them. The use of network telemetry is dramatically expanding and new risks and vulnerabilities are challenging issues for telemetry networks. Most of these vulnerabilities are silent in nature and c
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Andrésen, Anton, and Adam Håkansson. "Comparing unsupervised clustering algorithms to locate uncommon user behavior in public travel data : A comparison between the K-Means and Gaussian Mixture Model algorithms." Thesis, Tekniska Högskolan, Jönköping University, JTH, Datateknik och informatik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-49243.

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Clustering machine learning algorithms have existed for a long time and there are a multitude of variations of them available to implement. Each of them has its advantages and disadvantages, which makes it challenging to select one for a particular problem and application. This study focuses on comparing two algorithms, the K-Means and Gaussian Mixture Model algorithms for outlier detection within public travel data from the travel planning mobile application MobiTime1[1]. The purpose of this study was to compare the two algorithms against each other, to identify differences between their outl
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Slávik, Ľuboš. "Dynamická faktorová analýza časových řad." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445469.

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Táto diplomová práca sa zaoberá novým prístupom k zhlukovaniu časových rád na základe dynamického faktorového modelu. Dynamický faktorový model je technika redukujúca dimenziu a rozširuje klasickú faktorovú analýzu o požiadavku autokorelačnej štruktúry latentných faktorov. Parametre modelu sa odhadujú pomocou EM algoritmu za použitia Kalmanovho filtra a vyhladzovača a taktiež sú aplikované nevyhnutné podmienky na model, aby sa stal identifikovateľným. Po tom, ako je v práci predstavený teoretický koncept prístupu, dynamický faktorový model je aplikovaný na skutočné pozorované časové rady a prá
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MAQSOOD, RABIA. "ANALYZING AND MODELING STUDENTS¿ BEHAVIORAL DYNAMICS IN CONFIDENCE-BASED ASSESSMENT." Doctoral thesis, Università degli Studi di Milano, 2020. http://hdl.handle.net/2434/699383.

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Confidence-based assessment is a two-dimensional assessment paradigm which considers the confidence or expectancy level a student has about the answer, to ascertain his/her actual knowledge. Several researchers have discussed the usefulness of this model over the traditional one-dimensional assessment approach, which takes the number of correctly answered questions as a sole parameter to calculate the test scores of a student. Additionally, some educational psychologists and theorists have found that confidence-based assessment has a positive impact on students’ academic performance, knowledge
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Xu, Sanlin, and SanlinXu@yahoo com. "Mobility Metrics for Routing in MANETs." The Australian National University. Faculty of Engineering and Information Technology, 2007. http://thesis.anu.edu.au./public/adt-ANU20070621.212401.

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A Mobile Ad hoc Network (MANET) is a collection of wireless mobile nodes forming a temporary network without the need for base stations or any other pre–existing network infrastructure. In a peer-to-peer fashion, mobile nodes can communicate with each other by using wireless multihop communication. Due to its low cost, high flexibility, fast network establishment and self-reconfiguration, ad hoc networking has received much interest during the last ten years. However, without a fixed infrastructure, frequent path changes cause significant numbers of routing packets to discover new paths, leadi
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Labounek, René. "Fúze simultánních EEG-FMRI dat za pomoci zobecněných spektrálních vzorců." Doctoral thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2018. http://www.nusl.cz/ntk/nusl-371799.

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Mnoho rozdílných strategií fúze bylo vyvinuto během posledních 15 let výzkumu simultánního EEG-fMRI. Aktuální dizertační práce shrnuje aktuální současný stav v oblasti výzkumu fúze simultánních EEG-fMRI dat a pokládá si za cíl vylepšit vizualizaci úkolem evokovaných mozkových sítí slepou analýzou přímo z nasnímaných dat. Dva rozdílné modely, které by to měly vylepšit, byly navrhnuty v předložené práci (tj. zobecněný spektrální heuristický model a zobecněný prostorovo-frekvenční heuristický model). Zobecněný frekvenční heuristický model využívá fluktuace relativního EEG výkonu v určitých frekve
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Wimberly, Brent. "Identification of spatiotemporal nutrient patterns and associated ecohydrological trends in the tampa bay coastal region." Honors in the Major Thesis, University of Central Florida, 2012. http://digital.library.ucf.edu/cdm/ref/collection/ETH/id/642.

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Improvements for environmental monitoring and assessment were achieved to advance our understanding of sea-land interactions and nutrient cycling in a coastal bay.; The comprehensive assessment techniques for monitoring of water quality of a coastal bay can be diversified via an extensive investigation of the spatiotemporal nutrient patterns and the associated eco-hydrological trends in a coastal urban region. With this work, it is intended to thoroughly investigate the spatiotemporal nutrient patterns and associated eco-hydrological trends via a two part inquiry of the watershed and its adjac
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Book chapters on the topic "K-Mode Clustering"

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Xie, Fanyi. "Semiconductor Scheduling Problem Based on K-Mode Clustering Algorithm." In Lecture Notes in Electrical Engineering. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0115-6_97.

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Purnami, Santi Wulan, Jasni Mohamad Zain, and Abdullah Embong. "Reduced Support Vector Machine Based on k-Mode Clustering for Classification Large Categorical Dataset." In Software Engineering and Computer Systems. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22191-0_61.

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Dobša, Jasminka, and Henk A. L. Kiers. "Improving Classification of Documents by Semi-supervised Clustering in a Semantic Space." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_14.

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AbstractIn the paper we propose a method for representation of documents in a semantic lower-dimensional space based on the modified Reduced k-means method which penalizes clusterings that are distant from classification of training documents given by experts. Reduced k-means (RKM) enables simultaneously clustering of documents and extraction of factors. By projection of documents represented in the vector space model on extracted factors, documents are clustered in the semantic space in a semi-supervised way (using penalization) because clustering is guided by classification given by experts,
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He, Zengyou, Shengchun Deng, and Xiaofei Xu. "Approximation Algorithms for K-Modes Clustering." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/978-3-540-37275-2_38.

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Bishnu, Partha Sarathi, and Vandana Bhattacherjee. "A Modified K-Modes Clustering Algorithm." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45062-4_7.

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Ammar, Asma, Zied Elouedi, and Pawan Lingras. "K-Modes Clustering Using Possibilistic Membership." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31718-7_61.

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Ferrante, Mauro, and Anna Maria Parroco. "Media and fake news: An analysis of citizens’ attitudes toward misinformation in European countries." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.35.

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The rapid changes determined by the rise of Internet and the recent development of social media in daily life have led to profound consequences on the quantity and quality of data made available and on the mechanisms of their dissemination. The rapid spread of on-line disinformation is one of the most discussed topic, and has been identified as one of the top-trends in modern societies by the World Economic Forum, also because of the link between these processes and political communication. Thanks to the availability of micro-data from the Flash Eurobarometer survey on “Fake news and disinform
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Luo, Huilan, Fansheng Kong, and Yixiao Li. "Combining Multiple Clusterings Via k-Modes Algorithm." In Advanced Data Mining and Applications. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_34.

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Di Nuzzo, Cinzia, and Salvatore Ingrassia. "Three-Way Spectral Clustering." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-09034-9_13.

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AbstractIn this paper, we present a spectral clustering approach for clustering three-way data. Three-way data concern data characterized by three modes: n units, p variables, and t different occasions. In other words, three-way data contain a t × p observed matrix for each statistical observation. The units generated by simultaneous observation of variables in different contexts are usually structured as three-way data, so each unit is basically represented as a matrix. In order to cluster the n units in K groups, the spectral clustering application to three-way data can be a powerful tool fo
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Zafar, Aizan, and K. Swarupa Rani. "Novel Initialization Strategy for K-modes Clustering Algorithm." In Proceedings of International Conference on Big Data, Machine Learning and Applications. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4788-5_8.

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Conference papers on the topic "K-Mode Clustering"

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Ni, Jincheng, Jin Zhao, Xianliang Wu, and Ping Wu. "Enhanced Symplectic Geometric Mode Decomposition via K-means Clustering for LFM Signal Denoising." In 2024 IEEE 7th International Conference on Electronic Information and Communication Technology (ICEICT). IEEE, 2024. http://dx.doi.org/10.1109/iceict61637.2024.10671332.

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Khunji, Hooreya A., and Ahmed M. Zeki. "Clustering Rental Houses in Bahrain Using K Modes Algorithm." In 2024 5th International Conference on Data Analytics for Business and Industry (ICDABI). IEEE, 2024. https://doi.org/10.1109/icdabi63787.2024.10800640.

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Semwal, Akshita, Anirudh Purohit, Pallava Joshi, Manisha Basera, Vihan Singh Bhakuni, and Manika Manwal. "Performance Evaluation of K-Means Clustering Using MapReduce Programming Model." In 2024 4th International Conference on Technological Advancements in Computational Sciences (ICTACS). IEEE, 2024. https://doi.org/10.1109/ictacs62700.2024.10841066.

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Jaishankar, Anirudh, Neha Jain, Andrew Hornback, Asma Khimani, Pavithra Avula, and May D. Wang. "Identifying Features for Keloid Scars Subtyping Using K-Modes Clustering." In 2024 46th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE, 2024. https://doi.org/10.1109/embc53108.2024.10782576.

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Khan, Mozammel H. A. "Internal-Cluster-Validation-Based Model Selection For k-Means Clustering." In 2024 28th International Computer Science and Engineering Conference (ICSEC). IEEE, 2024. https://doi.org/10.1109/icsec62781.2024.10770636.

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Lima, Patrick S., Leonardo S. Souza, Leizer Schnitman, and Idelfonso B. R. Nogueira. "Application of K-means for Identification of Multiphase Flows Based on Computational Fluid Dynamics." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.124524.

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This study explores multiphase flow dynamics with a focus on the annular flow regime using Computational Fluid Dynamics (CFD) simulations. The methodology included defining the physical model, generating the computational mesh, and analyzing flow patterns. The Volume of Fluid (VOF) model captured fluid interactions, while the k-? SST turbulence model ensured accurate flow predictions. Simulations examined mixture density behavior and identified optimal configurations. A dataset was generated and analyzed using k-means clustering to classify flow patterns effectively. The results demonstrate th
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Li, Zhetong. "Flood Hazard Prediction Model Based on K-means Clustering Analysis and AdaBoost Regression Model." In 2025 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE). IEEE, 2025. https://doi.org/10.1109/edpee65754.2025.00036.

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Rajmohan, R., A. Meiappane, Chirag Gupta, and Shivam Bhavsar. "Intelligent SSH Attack Detection Model Using K-Clique Clustering and Reinforcement Learning." In 2024 International Conference on System, Computation, Automation and Networking (ICSCAN). IEEE, 2024. https://doi.org/10.1109/icscan62807.2024.10894529.

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Jia, Xibin, Jianming Yuan, and Yujie Xiao. "An Improved K - Mode Algorithm for Facial Expression Image Clustering." In 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016). Atlantis Press, 2016. http://dx.doi.org/10.2991/aiie-16.2016.65.

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Drude, Lukas, Christoph Boeddeker, and Reinhold Haeb-Umbach. "Blind speech separation based on complex spherical k-mode clustering." In 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2016. http://dx.doi.org/10.1109/icassp.2016.7471653.

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Reports on the topic "K-Mode Clustering"

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Subramanian, Balakrishnan. Opinion mining for breast cancer disease using a priori and K-modes clustering algorithm. Peeref, 2023. http://dx.doi.org/10.54985/peeref.2304p5680995.

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Eshed-Williams, Leor, and Daniel Zilberman. Genetic and cellular networks regulating cell fate at the shoot apical meristem. United States Department of Agriculture, 2014. http://dx.doi.org/10.32747/2014.7699862.bard.

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The shoot apical meristem establishes plant architecture by continuously producing new lateral organs such as leaves, axillary meristems and flowers throughout the plant life cycle. This unique capacity is achieved by a group of self-renewing pluripotent stem cells that give rise to founder cells, which can differentiate into multiple cell and tissue types in response to environmental and developmental cues. Cell fate specification at the shoot apical meristem is programmed primarily by transcription factors acting in a complex gene regulatory network. In this project we proposed to provide si
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Multiple Engine Faults Detection Using Variational Mode Decomposition and GA-K-means. SAE International, 2022. http://dx.doi.org/10.4271/2022-01-0616.

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As a critical power source, the diesel engine is widely used in various situations. Diesel engine failure may lead to serious property losses and even accidents. Fault detection can improve the safety of diesel engines and reduce economic loss. Surface vibration signal is often used in non-disassembly fault diagnosis because of its convenient measurement and stability. This paper proposed a novel method for engine fault detection based on vibration signals using variational mode decomposition (VMD), K-means, and genetic algorithm. The mode number of VMD dramatically affects the accuracy of ext
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