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Relevant bibliographies by topics / Micro-clustering

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Journal articles
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Academic literature on the topic 'Micro-clustering'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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Journal articles on the topic "Micro-clustering"

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Nakahara, Takanobu, Takeaki Uno, and Yukinobu Hamuro. "Prediction Model Using Micro-clustering." Procedia Computer Science 35 (2014): 1488–94. http://dx.doi.org/10.1016/j.procs.2014.08.231.

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Gupta, Mukul, Pradeep Kumar, and Bharat Bhasker. "Clustering of Micro-Messages Using Similarity Upper Approximation." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 01 (2017): 53–79. http://dx.doi.org/10.1142/s0218488517500039.

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Microblogging platforms like Twitter, Tumblr and Plurk have radically changed our lives. The presence of millions of people has made these platforms a preferred channel for communication. A large amount of User Generated Content, on these platforms, has attracted researchers and practitioners to mine and extract information nuggets. For information extraction, clustering is an important and widely used mining operation. This paper addresses the issue of clustering of micro-messages and corresponding users based on the text content of micro-messages that reflect their primitive interest. In thi

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Lin Li. "New Hybrid Clustering Algorithm for Micro Blog." INTERNATIONAL JOURNAL ON Advances in Information Sciences and Service Sciences 4, no. 23 (2012): 788–95. http://dx.doi.org/10.4156/aiss.vol4.issue23.97.

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Widyastuti, Christine, Adri Senen, and Oktaria Handayani. "Micro-Spatial Electricity Load Forecasting Clustering Technique." E3S Web of Conferences 202 (2020): 11005. http://dx.doi.org/10.1051/e3sconf/202020211005.

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Low growth of electricity load forecast eliminates cost opportunity of electricity sale due to unserviceable load demands. Meanwhile, if it is exorbitant, it will cause over-investment and incriminate investment cost. Existing method of sector load is simplified and easy to implement. However, the accuracy tends to bias over one area of which data is limited and dynamic service area. Besides, the results of its forecast is macro-based, which means it is unable to show load centres in micro grids and failed to locate the distribution station. Therefore, we need micro-spatial load forecasting. B

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K, Shyam Sunder Reddy, and Shoba Bindu C. "MCDAStream: a real-time data stream clustering based on micro-cluster density and attraction." International Journal of Engineering & Technology 7, no. 2 (2018): 270. http://dx.doi.org/10.14419/ijet.v7i2.9051.

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Real-time data stream clustering has been widely used in many fields, and it can extract useful information from massive sets of data. Most of the existing density-based algorithms cluster the data streams based on the density within the micro-clusters. These algorithms completely omit the data density in the area between the micro-clusters and recluster the micro-clusters based on erroneous assumptions about the distribution of the data within and between the micro-clusters that lead to poor clustering results. This paper describes a novel density-based clustering algorithm for evolving data

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Augustine, Donia. "A Survey on Density based Micro-clustering Algorithms for Data Stream Clustering." International Journal of Advanced Research in Computer Science and Software Engineering 7, no. 1 (2017): 186–90. http://dx.doi.org/10.23956/ijarcsse/v7i1/0111.

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Chen, Xinquan, and Yirou Qiu. "A Combined Clustering Algorithm Based on ESynC Algorithm and a Merging Judgement Process of Micro-Clusters." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 29, no. 03 (2021): 463–95. http://dx.doi.org/10.1142/s0218488521500203.

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The first synchronization clustering (SynC) algorithm based on an extensive Kuromato model was presented in 2010. In 2017, an effective synchronization clustering (ESynC) algorithm, inspired by SynC algorithm and a linear version of Vicsek model, was proposed. When facing complex data distributions, ESynC algorithm may regard an irregular and whole cluster as some micro-clusters. To conquer this shortcoming, a combined clustering algorithm based on ESynC algorithm and a merging judgement process of micro-clusters (CESynC) is presented. CESynC algorithm first uses ESynC algorithm to detect clus

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Murakami, Daisuke, and Yoshiki Yamagata. "Micro grids clustering for electricity sharing: an approach considering micro urban structure." Energy Procedia 142 (December 2017): 2748–53. http://dx.doi.org/10.1016/j.egypro.2017.12.220.

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Riyadh, Musaab, Norwati Mustapha, Md Nasir Sulaiman, and Nurfadhlina Binti Mohd Sharef. "CC_TRS: Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life." Mathematical Problems in Engineering 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/7523138.

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The rapid spreading of positioning devices leads to the generation of massive spatiotemporal trajectories data. In some scenarios, spatiotemporal data are received in stream manner. Clustering of stream data is beneficial for different applications such as traffic management and weather forecasting. In this article, an algorithm for Continuous Clustering of Trajectory Stream Data Based on Micro Cluster Life is proposed. The algorithm consists of two phases. There is the online phase where temporal micro clusters are used to store summarized spatiotemporal information for each group of similar

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Fazelabdolabadi, Babak, and Mohammad Hossein Golestan. "Towards Bayesian Quantification of Permeability in Micro-scale Porous Structures – The Database of Micro Networks." HighTech and Innovation Journal 1, no. 4 (2020): 148–60. http://dx.doi.org/10.28991/hij-2020-01-04-02.

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This article develops a Bayesian framework to quantify the absolute permeability of water in a porous structure from the geometry and clustering parameters of its underlying pore-throat network. These parameters include the network`s diameter, transivity, degree, centrality, assortativity, edge density, K-core decomposition, Kleinberg’s hub centrality scores, Kleinberg's authority centrality scores, length, and porosity. In addition, the incorporated clustering aspects of the networks have been determined with respect to several clustering criteria – edge betweenness, greedy optimization of mo

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Dissertations / Theses on the topic "Micro-clustering"

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Zhang, Weihua. "Genepart algorithm, clustering and feature selection for DNA micro-array data." Thesis, Montana State University, 2004. http://etd.lib.montana.edu/etd/2004/zhang/ZhangW1204.pdf.

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gona, rishitha. "Application of Micro Cloud for Cooperative Vehicles." OpenSIUC, 2020. https://opensiuc.lib.siu.edu/theses/2730.

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The emerging concept of vehicle cloudification is a promising solution to deal with ever-growing computational and communication demands of connected vehicles. A key idea is to have connected vehicles in the vicinity form a cluster which is called vehicular micro cloud. Vehicles in this micro cloud collaborate with other cluster members over vehicle-to-vehicle (V2V) networks for collective data processing, shared data storage, collaborative sensing and communication services. A typical use case of vehicular micro cloud is creation of a regional distributed data storage service, where member ve

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Crossman, Nathaniel C. "Stream Clustering And Visualization Of Geotagged Text Data For Crisis Management." Wright State University / OhioLINK, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=wright1590957641168863.

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Petraro, Alessandro. "Clustering di tracce di mobilità per l’identificazione di stili di guida." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/13003/.

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Traffic simulators are effective tools to support decisions in urban planning systems, to identify criticalities, to observe emerging behaviours in road networks and to configure road infrastructures, such as road side units and traffic lights. Clearly the more realistic the simulator the more precise the insight provided to decision makers. This paper provides a first step toward the design and calibration of traffic micro-simulator to produce realistic behaviour. The long term idea is to collect and analyse real traffic traces collecting vehicular information, to cluster them in groups repre

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Sobíšek, Lukáš. "Shluková a regresní analýza mikropanelových dat." Doctoral thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-261941.

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The main purpose of panel studies is to analyze changes in values of studied variables over time. In micro panel research, a large number of elements are periodically observed within the relatively short time period of just a few years. Moreover, the number of repeated measurements is small. This dissertation deals with contemporary approaches to the regression and the clustering analysis of micro panel data. One of the approaches to the micro panel analysis is to use multivariate statistical models originally designed for crosssectional data and modify them in order to take into account the w

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Panayiotou, Helen. "Vibrational spectroscopy of keratin fibres : A forensic approach." Queensland University of Technology, 2004. http://eprints.qut.edu.au/15953/.

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Human hair profiling is an integral part of a forensic investigation but it is one of the most technically difficult subjects in forensic science. This thesis describes the research and development of a novel approach for the rapid identification of unknown human and other related keratin fibres found at a crime scene. The work presented here is developed systematically and considers sample collection, sample preparation, analysis and interpretation of spectral data for the profiling of hair fibres encountered in criminal cases. Spectral comparison of fibres was facilitated with the use of

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Essa, Zahi. "Physical modelling of impurity diffusion and clustering phenomena in CMOS based image sensors." Phd thesis, Université Paul Sabatier - Toulouse III, 2013. http://tel.archives-ouvertes.fr/tel-01020497.

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L'essor de l'industrie micro-électronique au cours des dernières années n'aurait pas été possible sans les innovations en termes de procédés de fabrication de la technologie CMOS (Complementary Metal Oxide Semiconductor) induisant une amélioration continue des performances des composants. Ces innovations doivent relever les défis technologiques inhérents à la fois à la miniaturisation ainsi qu'à la diversification croissante des composants. En réponse à ces défis, des approches de modélisation de type TCAD (Technology Computer Aided Design), permettent de réduire nettement le temps et le coût

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Villegas, Navarro Fernanda. "Micro/nanometric Scale Study of Energy Deposition and its Impact on the Biological Response for Ionizing Radiation : Brachytherapy radionuclides, proton and carbon ion beams." Doctoral thesis, Uppsala universitet, Medicinsk strålningsvetenskap, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-279385.

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Research in radiotherapy for cancer treatment focuses on finding methods that can improve the compromise between tumour cell inactivation versus damage to the surrounding healthy tissue. As new radiation modalities such as proton therapy become accessible for everyday clinical practice, a better understanding of the variation in biological response of the tumour and healthy tissues would improve treatment planning to achieve optimal outcome. The development of radiobiological models capable of accurate predictions of biological effectiveness is needed. Existing radiation quality descriptors su

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Lin, Chun-Hsueh, and 林俊學. "Hierarchical Clustering on Streaming Data Using Micro Cluster." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/44574921887305511185.

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碩士<br>國立清華大學<br>資訊工程學系<br>97<br>隨著科技的進步，許多新發展出來的應用環境處理的資料都是以串流的型式出現，而非傳統資庫所處理的資料集合。這一類型資料分析的研究議題在近幾年間愈來愈受到矚目。這些議題中，在資料串流上分群由於被使用在許多應用環境中，例如行為分析，而顯得特別地重要。本篇論文的研究主要在於資料串流上同時被要求多重分群任務的處理。在這樣的環境之中，我們指出現有文獻所使用的分群方法有其效率或效能上的不足之處。因此在這篇論文中，我們發展一種新的分群演算法來有效率地同時處理多重分群的任務。這個演算法是基於階層式分群的方法，也就是逐步合併較相似的群集，來達到有效率地處理多重分群任務之目的。透過模擬群集所產生資料所做的實驗表現出，使用這個演算法可以有效率地執行在資料串流上的分群任務。

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Su, Chun-Chieh, and 蘇俊杰. "A Novel Hierarchical Clustering Algorithm Using the Micro-Cluster Strategy." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/01818566982120265505.

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碩士<br>中原大學<br>資訊管理研究所<br>100<br>This paper proposes a novel Hierarchical Clustering algorithm based on the Micro-Cluster strategy, called HCMC algorithm. In order to alleviate the influence caused by noise on the quality of clustering, the concept of density-based is applied to data partitioning and filtration of noise ratio. HCMC algorithm consists in two phases clustering. The first phase aims to partition several micro-clusters and to filter noise ratio with the operation of density-based, which saves the main materials of clusters. The second phase proceeds to encapsulate, applying single-

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Book chapters on the topic "Micro-clustering"

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Tsagkalidou, Katerina, Vassiliki Koutsonikola, Athena Vakali, and Konstantinos Kafetsios. "Emotional Aware Clustering on Micro-blogging Sources." In Affective Computing and Intelligent Interaction. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24600-5_42.

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Dutta, Soumi, Sujata Ghatak, Asit Kumar Das, Manan Gupta, and Sayantika Dasgupta. "Feature Selection-Based Clustering on Micro-blogging Data." In Advances in Intelligent Systems and Computing. Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-10-8055-5_78.

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Pijaudier-Cabot, G. "Micro-Crack Clustering, Non Local and Gradient Damage Models." In Damage and Fracture of Disordered Materials. Springer Vienna, 2000. http://dx.doi.org/10.1007/978-3-7091-2504-5_5.

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Amini, Amineh, and Teh Ying Wah. "A Comparative Study of Density-based Clustering Algorithms on Data Streams: Micro-clustering Approaches." In Lecture Notes in Electrical Engineering. Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1695-1_21.

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Ibrus, Indrek. "Micro-studios Meet Convergence Culture: Crossmedia, Clustering, Dialogues, Auto-communication." In Media Convergence Handbook - Vol. 2. Springer Berlin Heidelberg, 2016. http://dx.doi.org/10.1007/978-3-642-54487-3_9.

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Luo, Zhongming, Zhuofu Liu, and Junfu Li. "Micro-Blood Vessel Detection Using K-means Clustering and Morphological Thinning." In Advances in Neural Networks – ISNN 2011. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-21111-9_39.

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Aroche-Villarruel, Argenis A., José Fco Martínez-Trinidad, Jesús Ariel Carrasco-Ochoa, and Airel Pérez-Suárez. "A Different Approach for Pruning Micro-clusters in Data Stream Clustering." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-19264-2_4.

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Mojeed, Hammed A., Ummu Sanoh, Shakirat A. Salihu, et al. "An Approach for Journal Summarization Using Clustering Based Micro-Summary Generation." In Software Engineering Perspectives in Intelligent Systems. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-63319-6_64.

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Vossough, Ehsan, and Janusz R. Getta. "Micro Implementation of Join Operation at Clustering Nodes of Heterogenous Sensor Networks." In Lecture Notes in Business Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01112-2_9.

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Papavasileiou, Evgenia, Frederik Temmermans, Bart Jansen, et al. "Shape-Based Clustering and Classification of Breast Microcalcifications in Micro-CT Images." In IFMBE Proceedings. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11128-5_40.

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Conference papers on the topic "Micro-clustering"

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Yamazaki, Tomoya, Nobuyuki Shimizu, Hayato Kobayashi, and Satoshi Yamauchi. "Weighted Micro-Clustering." In the 25th International Conference Companion. ACM Press, 2016. http://dx.doi.org/10.1145/2872518.2889406.

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Uno, Takeaki, Hiroki Maegawa, Takanobu Nakahara, Yukinobu Hamuro, Ryo Yoshinaka, and Makoto Tatsuta. "Micro-clustering by data polishing." In 2017 IEEE International Conference on Big Data (Big Data). IEEE, 2017. http://dx.doi.org/10.1109/bigdata.2017.8258024.

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Hashimoto, Takako, Akira Kusaba, Dave Shepard, Tetsuji Kuboyama, Kilho Shin, and Takeaki Uno. "Twitter Topic Progress Visualization using Micro-clustering." In 9th International Conference on Pattern Recognition Applications and Methods. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009160805850592.

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Petraro, Alessandro, Federico Caselli, Michela Milano, and Marco Lippi. "Driving Behaviour Clustering For Realistic Traffic Micro-Simulators." In 31st Conference on Modelling and Simulation. ECMS, 2017. http://dx.doi.org/10.7148/2017-0018.

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Hashimoto, Takako, Takeaki Uno, Tetsuji Kuboyama, Kilho Shin, and Dave Shepard. "Time Series Topic Transition Based on Micro-Clustering." In 2019 IEEE International Conference on Big Data and Smart Computing (BigComp). IEEE, 2019. http://dx.doi.org/10.1109/bigcomp.2019.8679255.

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Tsur, Oren, Adi Littman, and Ari Rappoport. "Scalable multi stage clustering of tagged micro-messages." In the 21st international conference companion. ACM Press, 2012. http://dx.doi.org/10.1145/2187980.2188157.

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Widyastuti, Christine, Adri Senen, and Oktaria Handayani. "Micro-Spatial Electricity Load Forecasting Using Clustering Technique." In 2020 2nd International Conference on Industrial Electrical and Electronics (ICIEE). IEEE, 2020. http://dx.doi.org/10.1109/iciee49813.2020.9277274.

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Chatzakou, Despoina, Vassiliki Koutsonikola, Athena Vakali, and Konstantinos Kafetsios. "Micro-blogging Content Analysis via Emotionally-Driven Clustering." In 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction (ACII). IEEE, 2013. http://dx.doi.org/10.1109/acii.2013.68.

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Ma, Ke, Lingjuan Li, Yimu Ji, Shengmei Luo, and Tao Wen. "Research on Parallelized Stream Data Micro Clustering Algorithm." In 2015 International Conference on Advances in Mechanical Engineering and Industrial Informatics. Atlantis Press, 2015. http://dx.doi.org/10.2991/ameii-15.2015.116.

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Jin, Chunxia, and Su Zhang. "Micro-blog Short Text Clustering Algorithm Based on Bootstrapping." In 2019 12th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2019. http://dx.doi.org/10.1109/iscid.2019.10143.

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