Relevant bibliographies by topics / Bisecting K

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

Academic literature on the topic 'Bisecting K'

Author: Grafiati
Published: 4 June 2025
Last updated: 1 August 2025

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Journal articles on the topic "Bisecting K"

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Puspitasari, Novianti, Joan Angelina Widians, and Noval Bayu Setiawan. "Customer segmentation using bisecting k-means algorithm based on recency, frequency, and monetary (RFM) model." Jurnal Teknologi dan Sistem Komputer 8, no. 2 (2019): 78–83. http://dx.doi.org/10.14710/jtsiskom.8.2.2020.78-83.

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Information on customer loyalty characteristics in a company is needed to improve service to customers. A customer segmentation model based on transaction data can provide this information. This study used parameters from the recency, frequency, and monetary (RFM) model in determining customer segmentation and bisecting k-means algorithm to determine the number of clusters. The dataset used 588 sales transactions for PT Dinar Energi Utama in 2017. The clusters formed by the bisecting k-means and k-means algorithm were tested using the silhouette coefficient method. The bisecting k-means algori
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Gohzali, Hernawati, and Dita Maria Panjaitan. "Movie Recommendation System Model using Bisecting K-Means Technique and Collaborative Filtering." Journal of Multimedia Trend and Technology 3, no. 2 (2024): 95–104. http://dx.doi.org/10.35671/jmtt.v3i2.71.

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In the current film industry, the competition is very big. We can see it in online streaming content through the ratings obtained. Film itself is a visual work that is packaged as a product of public entertainment for a specific purpose. However, there are also many films that are considered not to meet the audience's expectations. Even the films presented are sometimes illegal or pirated films. We can also find out whether a film is recommended or not. The problem is that viewers rarely understand how to see recommendations or even provide appropriate film recommendations. This study aims to
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Hisyam, Masfu, Ali Ridho Barakbah, Iwan Syarif, and Ferry Astika S. "Spatio Temporal with Scalable Automatic Bisecting-Kmeans for Network Security Analysis in Matagaruda Project." EMITTER International Journal of Engineering Technology 7, no. 1 (2019): 83–104. http://dx.doi.org/10.24003/emitter.v7i1.340.

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Internet attacks are a frequent occurrence and the incidence is always increasing every year, therefore Matagaruda project is built to monitor and analyze internet attacks using IDS (Intrusion Detection System). Unfortunately, the Matagaruda project has lacked in the absence of trend analysis and spatiotemporal analysis. It causes difficulties to get information about the usual seasonal attacks, then which sector is the most attacked and also the country or territory where the internet attack originated. Due to the number of unknown clusters, this paper proposes a new method of automatic bisec
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Patil, Ruchika, and Amreen Khan. "Bisecting K-Means for Clustering Web Log data." International Journal of Computer Applications 116, no. 19 (2015): 36–41. http://dx.doi.org/10.5120/20448-2799.

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Li, Yanjun, and Soon M. Chung. "Parallel bisecting k-means with prediction clustering algorithm." Journal of Supercomputing 39, no. 1 (2007): 19–37. http://dx.doi.org/10.1007/s11227-006-0002-7.

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Dwididanti, Shinta, and Dimas Aryo Anggoro. "Analisis Perbandingan Algoritma Bisecting K-Means dan Fuzzy C-Means pada Data Pengguna Kartu Kredit." Emitor: Jurnal Teknik Elektro 22, no. 2 (2022): 110–17. http://dx.doi.org/10.23917/emitor.v22i2.15677.

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Di era digital seperti sekarang ini memiliki kartu kredit merupakan suatu hal yang wajar di masyarakat, dengan segala kemudahan yang ditawarkan dalam setiap transaksi pembayaran tidak menutup kemungkinan untuk menarik minat masyarakat dalam menggunakan kartu kredit. Dengan minat masyarakat yang tinggi terhadap kartu kredit, hal ini dapat dijadikan sebagai indikator yang baik bagi perusahaan kartu kredit untuk mengembangkan bisnis kartu kredit. Dalam rangka memenuhi kebutuhan konsumen akan kartu kredit, perusahaan dituntut untuk mengambil keputusan dalam menentukan strategi pemasaran yang tepat
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Phang, Elvina Lorenza. "Comparison Of Bisecting K-Means And K-Medoids Algorithm In Grouping Junior High School Students Based On Bebras Challenge 2023 Results." Prosiding Seminar Nasional KONSTELASI 2, no. 1 (2025): 20–31. https://doi.org/10.24002/prosidingkonstelasi.v2i1.11162.

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Improving the quality of education, especially Computational Thinking (CT) skills, is an important priority in the digital era. Bebras Challenge, an international competition to measure Computational Thinking. The results show the diverse abilities of Junior High School students, so that appropriate mentoring methods are needed. This study compares the Bisecting K-Means and K-Medoids algorithms to group students based on the results of the 2023 Bebras Challenge, along with their Indonesian, Mathematics, Science scores, and the duration of competition preparation. The study was conducted throug
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Dai, Hua, Xuelong Dai, Xiao Li, Xun Yi, Fu Xiao, and Geng Yang. "A Multibranch Search Tree-Based Multi-Keyword Ranked Search Scheme over Encrypted Cloud Data." Security and Communication Networks 2020 (January 23, 2020): 1–15. http://dx.doi.org/10.1155/2020/7307315.

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In the interest of privacy concerns, cloud service users choose to encrypt their personal data before outsourcing them to cloud. However, it is difficult to achieve efficient search over encrypted cloud data. Therefore, how to design an efficient and accurate search scheme over large-scale encrypted cloud data is a challenge. In this paper, we integrate bisecting k-means algorithm and multibranch tree structure and propose the α-filtering tree search scheme based on bisecting k-means clusters. The novel index tree is built from bottom-up, and a greedy depth first algorithm is used for filterin
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Rana, Deepak Singh. "Generating Document Summary using Data Mining and Clustering Techniques." Mathematical Statistician and Engineering Applications 70, no. 1 (2021): 285–92. http://dx.doi.org/10.17762/msea.v70i1.2310.

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 This paper presents a novel approach to generating document summaries using data mining and clustering techniques, specifically K-means clustering and bisecting K-means clustering algorithms. With the exponential growth of textual data, there is an increasing need for efficient and accurate summarization techniques to aid users in understanding the key information within large collections of documents. This study explores the potential of data mining and clustering methods in extracting salient features from textual data and producing high-quality sum
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Sun, D., H. Fei, and Q. Li. "A Bisecting K-Medoids clustering Algorithm Based on Cloud Model." IFAC-PapersOnLine 51, no. 11 (2018): 308–15. http://dx.doi.org/10.1016/j.ifacol.2018.08.301.

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Dissertations / Theses on the topic "Bisecting K"

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Li, Yanjun. "High Performance Text Document Clustering." Wright State University / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=wright1181005422.

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Jiménez, González Daniel. "ALGORITMOS DE CLUSTERING PARALELOS EN SISTEMAS DE RECUPERACIÓN DE INFORMACIÓN DISTRIBUIDOS." Doctoral thesis, Universitat Politècnica de València, 2011. http://hdl.handle.net/10251/11234.

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La información es útil si cuando se necesita está disponible y se puede hacer uso de ella. La disponibilidad suele darse fácilmente cuando la información está bien estructurada y ordenada, y además, no es muy extensa. Pero esta situación no es la más común, cada vez se tiende más a que la cantidad de información ofrecida crezca de forma desmesurada, que esté desestructurada y que no presente un orden claro. La estructuración u ordenación manual es inviable debido a las dimensiones de la información a manejar. Por todo ello se hace clara la utilidad, e incluso la necesidad, de buenos si
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Hinz, Joel. "Clustering the Web : Comparing Clustering Methods in Swedish." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-95228.

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Clustering -- automatically sorting -- web search results has been the focus of much attention but is by no means a solved problem, and there is little previous work in Swedish. This thesis studies the performance of three clustering algorithms -- k-means, agglomerative hierarchical clustering, and bisecting k-means -- on a total of 32 corpora, as well as whether clustering web search previews, called snippets, instead of full texts can achieve reasonably decent results. Four internal evaluation metrics are used to assess the data. Results indicate that k-means performs worse than the other tw
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Book chapters on the topic "Bisecting K"

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Chen, Yuxing, Wei Liu, Haojie Zhao, Shuling Cao, Shasha Fu, and Dingde Jiang. "Bisecting K-Means Based Fingerprint Indoor Localization." In Simulation Tools and Techniques. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32216-8_1.

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Zi, Ye, Liang Kun, Zhiyuan Zhang, Chunfeng Wang, and Zhe Peng. "An Improved Bisecting K-Means Text Clustering Method." In Advances in Intelligent Systems and Computing. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-34387-3_19.

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Chitralegha, M., and K. Thangavel. "Bisecting K-Means Initialization Technique for Protein Sequence Motif Identification." In Mining Intelligence and Knowledge Exploration. Springer International Publishing, 2013. http://dx.doi.org/10.1007/978-3-319-03844-5_14.

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Archetti, F., P. Campanelli, E. Fersini, and E. Messina. "A Hierarchical Document Clustering Environment Based on the Induced Bisecting k-Means." In Flexible Query Answering Systems. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11766254_22.

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Hatagami, Yutaro, and Toshihiko Matsuka. "Text Mining with an Augmented Version of the Bisecting K-Means Algorithm." In Neural Information Processing. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-10684-2_39.

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Frieze, Alan, and Mark Jerrum. "Improved approximation algorithms for MAX k-CUT and MAX BISECTION." In Integer Programming and Combinatorial Optimization. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/3-540-59408-6_37.

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Napoleon, D., M. Praneesh, S. Sathya, and M. SivaSubramani. "An Efficient Numerical Method for the Prediction of Clusters Using K-Means Clustering Algorithm with Bisection Method." In Communications in Computer and Information Science. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29216-3_28.

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Wu, Yushu, Fenfen Xie, Lu Wang, Shoude Zhang, Lei Zhang, and Xiaoying Wang. "Research on Characteristics of Chinese Herbal Medicine Compounds Based on Bisecting k-Means Algorithm." In Fuzzy Systems and Data Mining VI. IOS Press, 2020. http://dx.doi.org/10.3233/faia200713.

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The properties of Chinese Herbal Medicine (CHM) are determined to some extent by the properties of their molecular compounds, so it is of great significance to study CHM from the perspective of molecular compounds. In this paper, the clustering algorithm in data mining is used to study the relationship between the properties of CHM and its chemical components. Firstly, the molecular data are collected from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform, and the data set is preprocessed to extract the key molecular descriptors of chemical components. Second
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"A Novel Approach towards Bisecting K-means Clustering Algorithm Parallelism." In International Conference on Computer Technology and Development, 3rd (ICCTD 2011). ASME Press, 2011. http://dx.doi.org/10.1115/1.859919.paper138.

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Sikorski, Krzysztof A. "Topological Degree Computation." In Optimal Solution of Nonlinear Equations. Oxford University Press, 2001. http://dx.doi.org/10.1093/oso/9780195106909.003.0008.

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In this chapter we address the problem of computing topological degree of Lipschitz functions. From the knowledge of the topological degree one may ascertain whether there exists a zero of a function inside the domain, a knowledge that is practically and theoretically worthwile. Namely, Kronecker’s theorem states that if the topological degree is not zero then there exists a zero of a function inside the domain. Under more-restrictive assumptions one may also derive equivalence statements, i.e., nonzero degree is equivalent to the existence of a zero. By computing a sequence of domains with no
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Conference papers on the topic "Bisecting K"

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Rohilla, Vinita, Ms Sanika Singh kumar, Sudeshna Chakraborty, and Ms Sanika Singh. "Data Clustering using Bisecting K-Means." In 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS). IEEE, 2019. http://dx.doi.org/10.1109/icccis48478.2019.8974537.

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Gao, Haoyuan. "A Peer-to-Peer Distributed Bisecting K-means." In ICMLC 2022: 2022 14th International Conference on Machine Learning and Computing. ACM, 2022. http://dx.doi.org/10.1145/3529836.3529895.

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Ridha, Ahmad, and Arizal Notyasa. "C source code clustering using bisecting K-means." In INTELLIGENT BIOTECHNOLOGIES OF NATURAL AND SYNTHETIC BIOLOGICALLY ACTIVE SUBSTANCES: XIV Narochanskie Readings. AIP Publishing, 2023. http://dx.doi.org/10.1063/5.0177501.

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Savaresi, Sergio M., and Daniel L. Boley. "On the performance of bisecting K-means and PDDP." In Proceedings of the 2001 SIAM International Conference on Data Mining. Society for Industrial and Applied Mathematics, 2001. http://dx.doi.org/10.1137/1.9781611972719.5.

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Tao, Yingjuan, Jinsheng Deng, and Xingshen Song. "Drug Audit Based on Bisecting K-Means Clustering Algorithm." In 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC). IEEE, 2019. http://dx.doi.org/10.1109/cyberc.2019.00052.

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Xiao-Zhang Liu and Guo-Can Feng. "Kernel Bisecting k-means clustering for SVM training sample reduction." In 2008 19th International Conference on Pattern Recognition (ICPR). IEEE, 2008. http://dx.doi.org/10.1109/icpr.2008.4761793.

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LIU, JIA, Xin Kang, Shun Nishide, and Fuji Ren. "Collaborative filtering recommendation algorithm based on bisecting K-means clustering." In International Symposium on Artificial Intelligence and Robotics (ISAIR), edited by Huimin Lu, Jože Guna, and Yujie Li. SPIE, 2020. http://dx.doi.org/10.1117/12.2580026.

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Singh, Harpreet, Sarabpreet Kaur, and Chetna Kaushal. "Text Document clustering using partial Fractionation and Bisecting K-means." In 2022 10th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). IEEE, 2022. http://dx.doi.org/10.1109/icrito56286.2022.9964710.

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Mahmud, M. R., M. A. Mamun, M. A. Hossain, and M. P. Uddin. "Comparative Analysis of K-Means and Bisecting K-Means Algorithms for Brain Tumor Detection." In 2018 International Conference on Computer, Communication, Chemical, Material and Electronic Engineering (IC4ME2). IEEE, 2018. http://dx.doi.org/10.1109/ic4me2.2018.8465607.

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Banerjee, Shreya, Ankit Choudhary, and Somnath Pal. "Empirical evaluation of K-Means, Bisecting K-Means, Fuzzy C-Means and Genetic K-Means clustering algorithms." In 2015 IEEE International WIE Conference on Electrical and Computer Engineering (WIECON-ECE). IEEE, 2015. http://dx.doi.org/10.1109/wiecon-ece.2015.7443889.

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