Literatura académica sobre el tema "Overlapping Clusters"
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Artículos de revistas sobre el tema "Overlapping Clusters"
Mirzaie, Mansooreh, Ahmad Barani, Naser Nematbakkhsh y Majid Mohammad-Beigi. "Bayesian-OverDBC: A Bayesian Density-Based Approach for Modeling Overlapping Clusters". Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/187053.
Texto completoSingh, Sukhminder. "Estimation in overlapping clusters". Communications in Statistics - Theory and Methods 17, n.º 2 (enero de 1988): 613–21. http://dx.doi.org/10.1080/03610928808829643.
Texto completoDanganan, Alvincent Egonia, Ariel M. Sison y Ruji P. Medina. "OCA: overlapping clustering application unsupervised approach for data analysis". Indonesian Journal of Electrical Engineering and Computer Science 14, n.º 3 (1 de junio de 2019): 1471. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1471-1478.
Texto completoDanganan, Alvincent E. y Edjie Malonzo De Los Reyes. "eHMCOKE: an enhanced overlapping clustering algorithm for data analysis". Bulletin of Electrical Engineering and Informatics 10, n.º 4 (1 de agosto de 2021): 2212–22. http://dx.doi.org/10.11591/eei.v10i4.2547.
Texto completoQing, Huan. "Studying Asymmetric Structure in Directed Networks by Overlapping and Non-Overlapping Models". Entropy 24, n.º 9 (30 de agosto de 2022): 1216. http://dx.doi.org/10.3390/e24091216.
Texto completoVidojević, Filip, Dušan Džamić y Miroslav Marić. "E-function for Fuzzy Clustering in Complex Networks". Ipsi Transactions on Internet research 18, n.º 1 (1 de enero de 2022): 17–21. http://dx.doi.org/10.58245/ipsi.tir.22jr.04.
Texto completoWu, Mary, Byung Chul Ahn y Chong Gun Kim. "A Channel Reuse Procedure in Clustering Sensor Networks". Applied Mechanics and Materials 284-287 (enero de 2013): 1981–85. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1981.
Texto completoAlaqtash, Mohammad, Moayad A.Fadhil y Ali F. Al-Azzawi. "A Modified Overlapping Partitioning Clustering Algorithm for Categorical Data Clustering". Bulletin of Electrical Engineering and Informatics 7, n.º 1 (1 de marzo de 2018): 55–62. http://dx.doi.org/10.11591/eei.v7i1.896.
Texto completoLee, Kyung-Soon. "Resampling Feedback Documents Using Overlapping Clusters". KIPS Transactions:PartB 16B, n.º 3 (30 de junio de 2009): 247–56. http://dx.doi.org/10.3745/kipstb.2009.16-b.3.247.
Texto completoAmdekar, S. J. "An Unbiased Estimator in Overlapping Clusters". Calcutta Statistical Association Bulletin 34, n.º 3-4 (septiembre de 1985): 231–32. http://dx.doi.org/10.1177/0008068319850312.
Texto completoTesis sobre el tema "Overlapping Clusters"
Sun, Haojun. "Determining the number of clusters and distinguishing overlapping clusters in data analysis". Thèse, Université de Sherbrooke, 2004. http://savoirs.usherbrooke.ca/handle/11143/5055.
Texto completoGesesse, Achamyeleh Dagnaw <1986>. "Automatic Extraction of Overlapping Camera Clusters for 3D Reconstruction". Master's Degree Thesis, Università Ca' Foscari Venezia, 2016. http://hdl.handle.net/10579/7516.
Texto completoDimitrov, Rossen Petkov. "Overlapping of communication and computation and early binding fundamental mechanisms for improving parallel performance on clusters of workstations /". Diss., Mississippi State : Mississippi State University, 2001. http://library.msstate.edu/etd/show.asp?etd=etd-04092001-231941.
Texto completoBeka, Sylvia Enobong. "The genomics of Type 1 Diabetes susceptibility regions and effect of regulatory SNPs". Thesis, University of Hertfordshire, 2016. http://hdl.handle.net/2299/17200.
Texto completoDas, Nivedita. "Modeling three-dimensional shape of sand grains using Discrete Element Method". [Tampa, Fla.] : University of South Florida, 2007. http://purl.fcla.edu/usf/dc/et/SFE0002072.
Texto completoTribou, Michael John. "Relative Pose Estimation Using Non-overlapping Multicamera Clusters". Thesis, 2014. http://hdl.handle.net/10012/8141.
Texto completoHuang, Ying-Shuo y 黃盈碩. "Non-exhaustive Clustering for Overlapping Patent Clusters Analysis". Thesis, 2007. http://ndltd.ncl.edu.tw/handle/15970598844632266402.
Texto completo國立清華大學
工業工程與工程管理學系
95
Facing the challenges from a knowledge-based economy, having a comprehensive understanding insights of technology and industry development is the basic and necessary requirement to gain competitive edges for enterprises. Consequently, enterprises apply technology forecasting techniques to assist generating useful information for further R&D strategic decisions. Nonetheless, current technology forecasting analyses base mostly on macro-indicators such as market share and growth rate rather than on specific technology development information, such as invention and patents of certain technology. In Campbell’s (1983) research, he found that patent documents often better expresses the development trend of technology when compare with ordinary scientific journals. Moreover, according to the report of WIPO (1996), patent documents can better reveal the core technology and innovation than other knowledge documents such as journal papers and technical reports. As a result, we try to incorporate patent analysis while proceed technology forecasting. In this research, a non-exhaustive clustering methodology is proposed as the basis for a novel technology forecasting system. Non-exhaustive clustering methodology allows overlapping of patent documents, which is plausible when any patent can claim multiple key technical inventions. The characteristic of non-exhaustivity emphasizes that one patent contains multiple technology breakthroughs. We use Radio Frequency Identification (RFID) as case example in this research. RFID ontology is constructed. Afterward, refined Normalized Term Frequency/Inverse Document Frequency (NTF-IDF) key-phrase extraction methodology is developed to extract representative key phrases for following clustering procedure. Finally, the non-exhaustive clustering methodology is applied to generate overlapping clusters of patents. The clustering results and analysis of growth trend for each cluster provide users a clear view of patent distribution in a given broad technology area (e.g., RFID). The expected results of this research contain extracting domain key phrases precisely, using the non-exhaustive clustering results as input data of technology forecasting and finally visualize the technology trend. This system enables R&D engineers and managers to find the existing patents related to their interested technical domains (clusters) and enable them to strengthen their R&D efforts offensively and defensively.
Li, Che-Yu y 李晢宇. "A Validity Index Method for Clusters with Different Densities and Overlapping". Thesis, 2015. http://ndltd.ncl.edu.tw/handle/gh9877.
Texto completo國立中興大學
資訊科學與工程學系
103
Validity Index is used to estimate the cluster quality, and find the correct number of clusters. In this thesis, we propose a new validity index, which is composed of the spreading measure and overlapping measure of clusters. The spreading measure is used to estimate the degree of dispersion of clusters in a dataset. The overall spreading is the sum of the spreading of all clusters. The overlapping measure is used to estimate the degree of isolation among all clusters. Lower overlapping means large separation between clusters. As a result, a good clustering result is expected to have lower overall spreading and lower overlapping measure. We conducted several experiments to validate the robustness of our validity index, including artificial datasets and public real datasets. Experimental results show that our validity index method has better tolerance for estimating the correct number of clusters with different densities and degrees of overlapping.
Wu, Cheng-Hshueh y 吳承學. "An Effective Validity Index Method for Gaussian-distributed Clusters of different sizes with various degrees of Dispersion and Overlapping". Thesis, 2016. http://ndltd.ncl.edu.tw/handle/94560761502870248336.
Texto completo國立中興大學
資訊科學與工程學系
104
Cluster validity index method has two significant functions: assessing the quality of clustering and finding the correct number in cluster grouping. In this thesis, we propose a cluster validity index method, which intends to reduce the problem of a cluster validity index method VDO having little tolerance on estimating correct number of clusters for datasets comprising unbalance-populated clusters. Our new method uses the clustering method siibFCM that can tolerate datasets comprising unbalance-populated clusters along with dispersion and overlapping measures for computing the cluster validity index. The dispersion measure is used to estimate the overall data density of clusters in the dataset. Smaller dispersion means that data points are distributed more closely in all clusters. The overlap measure represents the overall separation between any pair of clusters in the dataset. Low degree of overlap means that clusters are well separated each other. By combining these two metrics, we obtain a good cluster validity index. We conducted several experiments to validate the effectiveness of our validity indexing method, including artificial datasets and public real datasets. Experimental results show that our validity indexing method can effectively and reliably estimate the correct/optimal number of clusters that widely differ in size, dispersion, and overlapping.
Ho, Ping-Hsuan y 何秉軒. "A Cluster Validity Indexing Method Based on Entropy for Solving Cluster Overlapping Problem". Thesis, 2014. http://ndltd.ncl.edu.tw/handle/42200508036155950902.
Texto completo國立中興大學
資訊科學與工程學系
102
Data clustering technique can be used in many fields, such as data mining, statistical data analysis, image analysis, pattern recognition and so on. A good clustering can get the benefits of data compression and computational reduction; however, it is unknown about how many clusters that a data set should be partitioned. Having a good guess for an initial number of clusters is highly desired for clustering. The way to find the optimal number of clusters is called cluster validity. In this paper, we propose a new cluster validity indexing method which tries to solve the cluster overlap problem. First, The Fuzzy C-Means algorithm is used to get the necessary information for calculating the validity index. Second, the weight of separation vague on the overlapping part of clusters is increased according to our entropy-based algorithm. Our approach can help increasing the accurate rate of validity index. To demonstrate the effectiveness of our proposed validity index method, we conducted several experiments and compared our method with other cluster validity indices. Experimental results showed that our method is superior to all other methods.
Libros sobre el tema "Overlapping Clusters"
Henning, C. Randall. Tangled Governance. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198801801.001.0001.
Texto completoPalfrey, Simon. Formaction. Editado por Henry S. Turner. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199641352.013.18.
Texto completoCapítulos de libros sobre el tema "Overlapping Clusters"
Khandekar, Rohit, Guy Kortsarz y Vahab Mirrokni. "Advantage of Overlapping Clusters for Minimizing Conductance". En LATIN 2012: Theoretical Informatics, 494–505. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29344-3_42.
Texto completoJanot, Christian y Jean-Marie Dubois. "Quasicrystals as Hierarchical Packing of Overlapping Clusters". En Quasicrystals, 183–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-05028-6_8.
Texto completoSchwarz, Michael, Simmo Saan, Helmut Seidl, Julian Erhard y Vesal Vojdani. "Clustered Relational Thread-Modular Abstract Interpretation with Local Traces". En Programming Languages and Systems, 28–58. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30044-8_2.
Texto completoTorra, Vicenç. "Towards Integrally Private Clustering: Overlapping Clusters for High Privacy Guarantees". En Privacy in Statistical Databases, 62–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13945-1_5.
Texto completoGao, Wei, Kam-Fai Wong, Yunqing Xia y Ruifeng Xu. "Clique Percolation Method for Finding Naturally Cohesive and Overlapping Document Clusters". En Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead, 97–108. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11940098_10.
Texto completoTjhi, William-Chandra y Lihui Chen. "A New Fuzzy Co-clustering Algorithm for Categorization of Datasets with Overlapping Clusters". En Advanced Data Mining and Applications, 328–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_36.
Texto completoSingh, Sarjinder. "Non-Overlapping, Overlapping, Post, and Adaptive Cluster Sampling". En Advanced Sampling Theory with Applications, 765–828. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-007-0789-4_9.
Texto completoYokoyama, Satoru. "Improving Algorithm for Overlapping Cluster Analysis". En Advanced Studies in Behaviormetrics and Data Science, 329–38. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-2700-5_20.
Texto completoYokoyama, Satoru, Atsuho Nakayama y Akinori Okada. "An Application of One-mode Three-way Overlapping Cluster Analysis". En Studies in Classification, Data Analysis, and Knowledge Organization, 193–200. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-10745-0_20.
Texto completoKim, Paul y Sangwook Kim. "A Discovery Technique of Overlapping Cluster in Self-Organizing Network". En Convergence and Hybrid Information Technology, 743–48. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32692-9_94.
Texto completoActas de conferencias sobre el tema "Overlapping Clusters"
Das, Sunanda, Shreya Chaudhuri y Asit K. Das. "Cluster analysis for overlapping clusters using genetic algorithm". En 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2016. http://dx.doi.org/10.1109/icrcicn.2016.7813542.
Texto completotava, Martin y Pavel Tvrdík. "Overlapping Non-dedicated Clusters Architecture". En 2009 International Conference on Computer Engineering and Technology (ICCET 2009). IEEE, 2009. http://dx.doi.org/10.1109/iccet.2009.66.
Texto completoAndersen, Reid, David F. Gleich y Vahab Mirrokni. "Overlapping clusters for distributed computation". En the fifth ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2124295.2124330.
Texto completoWu, You, Xiong Wang, Zhe Yang, Xiaoying Gan, Xiaohua Tian y Xinbing Wang. "Crowdclustering items into overlapping clusters". En ICC 2016 - 2016 IEEE International Conference on Communications. IEEE, 2016. http://dx.doi.org/10.1109/icc.2016.7511257.
Texto completoAydin, Nevin, Farid Nait-Abdesselam, Volodymyr Pryyma y Damla Turgut. "Overlapping Clusters Algorithm in Ad Hoc Networks". En GLOBECOM 2010 - 2010 IEEE Global Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/glocom.2010.5683454.
Texto completoGoldberg, Mark K., Mykola Hayvanovych y Malik Magdon-Ismail. "Measuring Similarity between Sets of Overlapping Clusters". En 2010 IEEE Second International Conference on Social Computing (SocialCom). IEEE, 2010. http://dx.doi.org/10.1109/socialcom.2010.50.
Texto completoHe, Xiao, Jing Feng, Bettina Konte, Son T. Mai y Claudia Plant. "Relevant overlapping subspace clusters on categorical data". En KDD '14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2623330.2623652.
Texto completoStava, Martin y Pavel Tvrdik. "Security System for Overlapping Non-dedicated Clusters". En 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications. IEEE, 2009. http://dx.doi.org/10.1109/ispa.2009.19.
Texto completoRipon, Kazi Shah Nawaz y M. N. H. Siddique. "Evolutionary multi-objective clustering for overlapping clusters detection". En 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983051.
Texto completoIvannikova, Elena, Anna V. Kononova y Timo Hamalainen. "Probabilistic group dependence approach for discovering overlapping clusters". En 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2016. http://dx.doi.org/10.1109/mlsp.2016.7738882.
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