Relevant bibliographies by topics / Histogram Clustering

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

Academic literature on the topic 'Histogram Clustering'

Author: Grafiati
Published: 3 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Histogram Clustering"

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Ambarwati, Ambarwati, and Edi Winarko. "Pengelompokan Berita Indonesia Berdasarkan Histogram Kata Menggunakan Self-Organizing Map." IJCCS (Indonesian Journal of Computing and Cybernetics Systems) 8, no. 1 (2014): 101. http://dx.doi.org/10.22146/ijccs.3500.

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AbstrakBerita merupakan sumber informasi yang dinantikan oleh manusia setiap harinya. Manusia membaca berita dengan kategori yang diinginkan. Jika komputer mampu mengelompokkan berita secara otomatis maka tentunya manusia akan lebih mudah membaca berita sesuai dengan kategori yang diinginkan. Pengelompokan berita yang berupa artikel secara otomatis sangatlah menarik karena mengorganisir artikel berita secara manual membutuhkan waktu dan biaya yang tidak sedikit.Tujuan penelitian ini adalah membuat sistem aplikasi untuk pengelompokkan artikel berita dengan menggunakan algoritma Self Organizing
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Billard, L., and Jaejik Kim. "Hierarchical clustering for histogram data." Wiley Interdisciplinary Reviews: Computational Statistics 9, no. 5 (2017): e1405. http://dx.doi.org/10.1002/wics.1405.

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Jing, Hui, Cong Li, Mei Fa Huang, and Fu Yun Liu. "A Fast Retrieval Method Based on K-Means Clustering for Mechanical Product Design." Advanced Materials Research 156-157 (October 2010): 98–101. http://dx.doi.org/10.4028/www.scientific.net/amr.156-157.98.

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Radius Angle Histogram (RAH) is useful method of retrieving 3D mechanical models. This method, however, not completely uses the information of radius angle histogram of the model. As a result, the retrieval precision is not very high enough. To improve the retrieval efficiency, K-means clustering method (KMM) is proposed in this paper. The radius angle histograms of the models are established first and then be served as inputs as KMM, respectively. By using KMM, the models can be classified and the results can be obtained. To validate the proposed method, an experiment is given. The results sh
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Sidorova, V. S. "Histogram clustering validation for multispectral image." Optoelectronics, Instrumentation and Data Processing 43, no. 1 (2007): 28–32. http://dx.doi.org/10.3103/s8756699007010049.

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Sengee, Nyamlkhagva, and Heung Kook Choi. "Brightness preserving weight clustering histogram equalization." IEEE Transactions on Consumer Electronics 54, no. 3 (2008): 1329–37. http://dx.doi.org/10.1109/tce.2008.4637624.

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Cho, Jae-Hyun. "Psychology Analysis using Color Histogram Clustering." Journal of the Korea institute of electronic communication sciences 8, no. 3 (2013): 415–20. http://dx.doi.org/10.13067/jkiecs.2013.8.3.415.

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Coşkun, Aysu, and Sándor Bilicz. "Data-Driven Clustering and Classification of Road Vehicle Radar Scattering Characteristics Using Histogram-Based RCS Features." Electronics 14, no. 4 (2025): 759. https://doi.org/10.3390/electronics14040759.

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This paper presents the clustering and classification of the radar scattering characteristics of vehicles under real-world driving conditions. The classification of 14 distinct vehicle types is achieved through statistical features derived from their radar cross-section (RCS) characteristics, represented as histograms. Various machine learning classification techniques are applied, and their performance is evaluated across different clustering scenarios. The results of the clustering algorithm are in line with the physics-based expectations on the scattering from different vehicle types. The c
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guojing, FENG Jun, YE Haosheng, and ZHOU Gang. "Retrieval-angle clustering histogram and clustering for 3D model retrieval." Journal of Image and Graphics 15, no. 11 (2010): 1644. http://dx.doi.org/10.11834/jig.20101101.

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Lan, Hong, and Shao Bin Jin. "An Improved Suppressed FCM Algorithm for Image Segmentation." Advanced Materials Research 712-715 (June 2013): 2349–53. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2349.

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Fuzzy C-Means clustering(FCM) algorithm plays an important role in image segmentation, but it is sensitive to noise because of not taking into account the spatial information. Addressing this problem, this paper presents an improved suppressed FCM algorithm based on the pixels and the spatial neighborhood information of the image. The algorithm combines the two-dimentional histogram and suppressed FCM algorithm together. First, construct a two-dimentional histogram instead of one-dimentional histogram, which can better distinguish the distribution of the object and background for noisy images.
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Seo, Bo Bae, and Young Joo Yoon. "A study on sparse k-means clustering for histogram-valued data." Korean Data Analysis Society 26, no. 5 (2024): 1317–29. http://dx.doi.org/10.37727/jkdas.2024.26.5.1317.

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In this paper, we investigate a sparse k-means clustering method for histogram-valued data. The distances between histogram-valued observations are defined using the Wasserstein-Kantorovich distances to group p-dimensional histogram-valued data. Clustering is performed using the sparse k-means clustering method with the distance matrix computed for each dimension. The proposed method maximizes the weighted sums of squared distances between clusters. For various value of k, we apply the sparse k-means clustering method and determine the optimal number of clusters with the Silhouette measure. Si
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Dissertations / Theses on the topic "Histogram Clustering"

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Ge, Fei. "The lattice Boltzmann method dedicated to image processing." Thesis, Lyon, 2020. http://www.theses.fr/2020LYSEI012.

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La méthode de Boltzmann sur réseau est un outil de simulation numérique dont la formulation à l'échelle mésoscopique permet d'éviter la résolution d'une équation différentielle, et repose sur des mécanismes de propagation et de collision au cours du temps, de distributions de particules se propageant sur un réseau régulier. Si les lois de conservation sont imposées en chaque nœud du réseau, alors la solution générée correspondra à la modélisation de phénomènes physiques à l'échelle macroscopique. Dans ce contexte la méthode de Boltzmann est tout à fait adaptée pour résoudre un problème de méca
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Zhuang, Yuwen. "Metric Based Automatic Event Segmentation and Network Properties Of Experience Graphs." The Ohio State University, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=osu1337372416.

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Elsayed, Elawady Mohamed. "Reflection Symmetry Detection in Images : Application to Photography Analysis." Thesis, Lyon, 2019. http://www.theses.fr/2019LYSES006/document.

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La symétrie est une propriété géométrique importante en perception visuelle qui traduit notre perception des correspondances entre les différents objets ou formes présents dans une scène. Elle est utilisée comme élément caractéristique dans de nombreuses applications de la vision par ordinateur (comme par exemple la détection, la segmentation ou la reconnaissance d'objets) mais également comme une caractéristique formelle en sciences de l'art (ou en analyse esthétique). D’importants progrès ont été réalisés ces dernières décennies pour la détection de la symétrie dans les images mais il reste
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Meß, Christian, and Timo Ropinski. "Efficient Acquisition and Clustering of Local Histograms for Representing Voxel Neighborhoods." University of Münster, Germany, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-92876.

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In the past years many interactive volume rendering techniques have been proposed, which exploit the neighboring environment of a voxel during rendering. In general on-the-fly acquisition of this environment is infeasible due to the high amount of data to be taken into account. To bypass this problem we propose a GPU preprocessing pipeline which allows to acquire and compress the neighborhood information for each voxel. Therefore, we represent the environment around each voxel by generating a local histogram (LH) of the surrounding voxel densities. By performing a vector quantization (VQ), the
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Khachatryan, Andranik [Verfasser], and K. [Akademischer Betreuer] Böhm. "Clustering-Initialized Adaptive Histograms and Probabilistic Cost Estimation for Query Optimization / Andranik Khachatryan. Betreuer: K. Böhm." Karlsruhe : KIT-Bibliothek, 2012. http://d-nb.info/1027141714/34.

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Jarolím, Jordán. "Analýza a získávání informací ze souboru dokumentů spojených do jednoho celku." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2018. http://www.nusl.cz/ntk/nusl-385929.

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This thesis deals with mining of relevant information from documents and automatic splitting of multiple documents merged together. Moreover, it describes the design and implementation of software for data mining from documents and for automatic splitting of multiple documents. Methods for acquiring textual data from scanned documents, named entity recognition, document clustering, their supportive algorithms and metrics for automatic splitting of documents are described in this thesis. Furthermore, an algorithm of implemented software is explained and tools and techniques used by this softwar
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Lin, Tsu-Chun, and 林子鈞. "An Efficient Hierarchical Clustering AlgorithmBased On Histogram Method." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/88919307324647869062.

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碩士<br>嶺東科技大學<br>資訊科技應用研究所<br>100<br>Clustering is a very important method in Data Mining techniques. Development of information technology, a lot of dataset had accumulated in life. How to find more interesting information from these data is the main work of The Data Mining. Many scholars have been research and development for the new clustering algorithms. In this paper, a new clustering algorithm is proposed. The new clustering algorithm is a hierarchical clustering algorithm named HTCA clustering method that based on histogram method and Otsu bi-level thresholding. According to the splitti
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Huang, Chih-Wei, and 黃峙瑋. "Statistical Histogram-Based K-Means Clustering for Image Segmentation." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/87381130893336702056.

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碩士<br>國立中正大學<br>資訊管理學系暨研究所<br>101<br>When K-means cluster is used for image segmentation, it is very time consuming. In this preliminary study, we proposed a novel statistical histogram-based K-means clustering to increase the computational performance and to achieve the same segmented results as K-means clustering. The proposed method uses the statistical histogram to obtain probability density function of pixels of a figure and segment with weighting for the same intensity. We combine histogram and K-means clustering together to improve the shortcoming of high computational cost for K-means
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Kim, Jaejik. "Dissimilarity measures for histogram-valued data and divisive clustering of symbolic objects." 2009. http://purl.galileo.usg.edu/uga%5Fetd/kim%5Fjaejik%5F200908%5Fphd.

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Ho, His-Yun, and 何錫昀. "A Study of Weighted Block-based Fuzzy C-means Clustering and Co-correlate Histogram Technique for Human MRI Image Segmentation." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/7x8h5e.

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碩士<br>國立臺中科技大學<br>資訊工程系碩士班<br>101<br>The Magnetic Resonance Images (MRI) is one of the common ways to display the cerebral structures. The Parkinson&apos;&apos;s disease may lead to the cerebral cells atrophy that includes Caudate nucleus, Putamen and Thalamus…etc. Segmentation result of the cerebral cells can more effectively and accurately help doctors to diagnose diseases and shorten time of diagnosing. Therefore, this thesis proposes two automatic schemes processing the automatic MRI image segmentation of cerebral cells in applying the research of medical image. The first proposed scheme i
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Books on the topic "Histogram Clustering"

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Rajakumar, P. S., S. Geetha, and T. V. Ananthan. Fundamentals of Image Processing. Jupiter Publications Consortium, 2023. http://dx.doi.org/10.47715/jpc.b.978-93-91303-80-8.

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"Fundamentals of Image Processing" offers a comprehensive exploration of image processing's pivotal techniques, tools, and applications. Beginning with an overview, the book systematically categorizes and explains the multifaceted steps and methodologies inherent to the digital processing of images. The text progresses from basic concepts like sampling and quantization to advanced techniques such as image restoration and feature extraction. Special emphasis is given to algorithms and models crucial to image enhancement, restoration, segmentation, and application. In the initial segments, the i
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Book chapters on the topic "Histogram Clustering"

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Chavent, Marie, and Paula Brito. "Divisive Clustering of Histogram Data." In Analysis of Distributional Data. Chapman and Hall/CRC, 2022. http://dx.doi.org/10.1201/9781315370545-6.

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Balzanella, Antonio, and Rosanna Verde. "Histogram-Based Clustering of Sensor Network Data." In Data Analysis and Applications 1. John Wiley & Sons, Inc., 2019. http://dx.doi.org/10.1002/9781119597568.ch2.

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Mballo, Chérif, and Edwin Diday. "Kolmogorov-Smirnov for Decision Trees on Interval and Histogram Variables." In Classification, Clustering, and Data Mining Applications. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_33.

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Shieh, Shu-Ling, Tsu-Chun Lin, and Yu-Chin Szu. "An Efficient Clustering Algorithm Based on Histogram Threshold." In Intelligent Information and Database Systems. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-28490-8_4.

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Buhmann, J. M., and M. Held. "On the Optimal Number of Clusters in Histogram Clustering." In Studies in Classification, Data Analysis, and Knowledge Organization. Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-642-55991-4_4.

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Verde, Rosanna, and Antonio Irpino. "Dynamic Clustering of Histogram Data: Using the Right Metric." In Selected Contributions in Data Analysis and Classification. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-73560-1_12.

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Bhavani, S., and N. Subhash Chandra. "Histogram Based Initial Centroids Selection for K-Means Clustering." In Data Management, Analytics and Innovation. Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-2600-6_38.

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Tian, Mengqiu, Qiao Yang, Andreas Maier, Ingo Schasiepen, Nicole Maass, and Matthias Elter. "Automatic Histogram-Based Initialization of K-Means Clustering in CT." In Bildverarbeitung für die Medizin 2013. Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36480-8_49.

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Yang, Ruixin, Kwang-Su Yang, Menas Kafatos, and X. Sean Wang. "Value Range Queries on Earth Science Data via Histogram Clustering." In Temporal, Spatial, and Spatio-Temporal Data Mining. Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/3-540-45244-3_6.

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Khachatryan, Andranik, Emmanuel Müller, Klemens Böhm, and Jonida Kopper. "Efficient Selectivity Estimation by Histogram Construction Based on Subspace Clustering." In Lecture Notes in Computer Science. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22351-8_22.

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Conference papers on the topic "Histogram Clustering"

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Jaubert, Olivier, Salman Mohammadi, Keith A. Goatman, et al. "Conformal Coronary Calcification Volume Estimation with Conditional Coverage via Histogram Clustering." In 2025 IEEE 22nd International Symposium on Biomedical Imaging (ISBI). IEEE, 2025. https://doi.org/10.1109/isbi60581.2025.10980751.

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Baber, Junaid, and Olivier Aycard. "3D-PSH: Lightweight 3D LiDAR Object Detection Using Adaptive Clustering and 3D Point Spatial Histograms." In 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2024. https://doi.org/10.1109/ictai62512.2024.00092.

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Benzel, Steven, and Ana Stanescu. "Histogram Methods for Unsupervised Clustering." In ACM SE '20: 2020 ACM Southeast Conference. ACM, 2020. http://dx.doi.org/10.1145/3374135.3385302.

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Sun, Rongshu, Jingiing Zhang, Wei Jiang, and Yuexin Hu. "Segmentation of Pop Music Based on Histogram Clustering." In 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI). IEEE, 2018. http://dx.doi.org/10.1109/cisp-bmei.2018.8633060.

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Sengee, N., B. Bazarragchaa, T. Y. Kim, and H. K. Choi. "Weight Clustering Histogram Equalization for Medical Image Enhancement." In 2009 IEEE International Conference on Communications Workshops. IEEE, 2009. http://dx.doi.org/10.1109/iccw.2009.5208082.

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Gad, Walaa K., and Mohamed S. Kamel. "Incremental clustering algorithm based on phrase-semantic similarity histogram." In 2010 International Conference on Machine Learning and Cybernetics (ICMLC). IEEE, 2010. http://dx.doi.org/10.1109/icmlc.2010.5580499.

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Melnyk, Roman A., and Yurii I. Kalychak. "Face image barcodes by distributed cumulative histogram and clustering." In 2020 IEEE 15th International Conference on Advanced Trends in Radioelectronics, Telecommunications and Computer Engineering (TCSET). IEEE, 2020. http://dx.doi.org/10.1109/tcset49122.2020.235589.

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Zhang, Jian, Rui Sun, Chuan Yang, and Mingli Song. "Progressive Image Color Neutralization Based on Adaptive Histogram Clustering." In 2009 International Conference on Image and Graphics (ICIG). IEEE, 2009. http://dx.doi.org/10.1109/icig.2009.70.

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Fang, Shun, Xin Chang, and Shiqian Wu. "Histogram-based Fuzzy C-Means Clustering for Image Binarization." In 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA). IEEE, 2021. http://dx.doi.org/10.1109/iciea51954.2021.9516141.

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"DIVISIVE MONOTHETIC CLUSTERING FOR INTERVAL AND HISTOGRAM-VALUED DATA." In International Conference on Pattern Recognition Applications and Methods. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003793502290234.

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