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Статті в журналах з теми "Overlapping Clusters"
Mirzaie, Mansooreh, Ahmad Barani, Naser Nematbakkhsh, and 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.
Повний текст джерелаSingh, Sukhminder. "Estimation in overlapping clusters." Communications in Statistics - Theory and Methods 17, no. 2 (January 1988): 613–21. http://dx.doi.org/10.1080/03610928808829643.
Повний текст джерелаDanganan, Alvincent Egonia, Ariel M. Sison, and Ruji P. Medina. "OCA: overlapping clustering application unsupervised approach for data analysis." Indonesian Journal of Electrical Engineering and Computer Science 14, no. 3 (June 1, 2019): 1471. http://dx.doi.org/10.11591/ijeecs.v14.i3.pp1471-1478.
Повний текст джерелаDanganan, Alvincent E., and Edjie Malonzo De Los Reyes. "eHMCOKE: an enhanced overlapping clustering algorithm for data analysis." Bulletin of Electrical Engineering and Informatics 10, no. 4 (August 1, 2021): 2212–22. http://dx.doi.org/10.11591/eei.v10i4.2547.
Повний текст джерелаQing, Huan. "Studying Asymmetric Structure in Directed Networks by Overlapping and Non-Overlapping Models." Entropy 24, no. 9 (August 30, 2022): 1216. http://dx.doi.org/10.3390/e24091216.
Повний текст джерелаVidojević, Filip, Dušan Džamić, and Miroslav Marić. "E-function for Fuzzy Clustering in Complex Networks." Ipsi Transactions on Internet research 18, no. 1 (January 1, 2022): 17–21. http://dx.doi.org/10.58245/ipsi.tir.22jr.04.
Повний текст джерелаWu, Mary, Byung Chul Ahn, and Chong Gun Kim. "A Channel Reuse Procedure in Clustering Sensor Networks." Applied Mechanics and Materials 284-287 (January 2013): 1981–85. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1981.
Повний текст джерелаAlaqtash, Mohammad, Moayad A.Fadhil, and Ali F. Al-Azzawi. "A Modified Overlapping Partitioning Clustering Algorithm for Categorical Data Clustering." Bulletin of Electrical Engineering and Informatics 7, no. 1 (March 1, 2018): 55–62. http://dx.doi.org/10.11591/eei.v7i1.896.
Повний текст джерелаLee, Kyung-Soon. "Resampling Feedback Documents Using Overlapping Clusters." KIPS Transactions:PartB 16B, no. 3 (June 30, 2009): 247–56. http://dx.doi.org/10.3745/kipstb.2009.16-b.3.247.
Повний текст джерелаAmdekar, S. J. "An Unbiased Estimator in Overlapping Clusters." Calcutta Statistical Association Bulletin 34, no. 3-4 (September 1985): 231–32. http://dx.doi.org/10.1177/0008068319850312.
Повний текст джерелаДисертації з теми "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.
Повний текст джерелаGesesse, 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.
Повний текст джерелаDimitrov, 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.
Повний текст джерелаBeka, 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.
Повний текст джерелаDas, 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.
Повний текст джерелаTribou, Michael John. "Relative Pose Estimation Using Non-overlapping Multicamera Clusters." Thesis, 2014. http://hdl.handle.net/10012/8141.
Повний текст джерелаHuang, Ying-Shuo, and 黃盈碩. "Non-exhaustive Clustering for Overlapping Patent Clusters Analysis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/15970598844632266402.
Повний текст джерела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, and 李晢宇. "A Validity Index Method for Clusters with Different Densities and Overlapping." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/gh9877.
Повний текст джерела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, and 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.
Повний текст джерела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, and 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.
Повний текст джерела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.
Книги з теми "Overlapping Clusters"
Henning, C. Randall. Tangled Governance. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198801801.001.0001.
Повний текст джерелаPalfrey, Simon. Formaction. Edited by Henry S. Turner. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199641352.013.18.
Повний текст джерелаЧастини книг з теми "Overlapping Clusters"
Khandekar, Rohit, Guy Kortsarz, and Vahab Mirrokni. "Advantage of Overlapping Clusters for Minimizing Conductance." In LATIN 2012: Theoretical Informatics, 494–505. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29344-3_42.
Повний текст джерелаJanot, Christian, and Jean-Marie Dubois. "Quasicrystals as Hierarchical Packing of Overlapping Clusters." In Quasicrystals, 183–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-05028-6_8.
Повний текст джерелаSchwarz, Michael, Simmo Saan, Helmut Seidl, Julian Erhard, and Vesal Vojdani. "Clustered Relational Thread-Modular Abstract Interpretation with Local Traces." In Programming Languages and Systems, 28–58. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30044-8_2.
Повний текст джерелаTorra, Vicenç. "Towards Integrally Private Clustering: Overlapping Clusters for High Privacy Guarantees." In Privacy in Statistical Databases, 62–73. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13945-1_5.
Повний текст джерелаGao, Wei, Kam-Fai Wong, Yunqing Xia, and Ruifeng Xu. "Clique Percolation Method for Finding Naturally Cohesive and Overlapping Document Clusters." In 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.
Повний текст джерелаTjhi, William-Chandra, and Lihui Chen. "A New Fuzzy Co-clustering Algorithm for Categorization of Datasets with Overlapping Clusters." In Advanced Data Mining and Applications, 328–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11811305_36.
Повний текст джерелаSingh, Sarjinder. "Non-Overlapping, Overlapping, Post, and Adaptive Cluster Sampling." In Advanced Sampling Theory with Applications, 765–828. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-007-0789-4_9.
Повний текст джерелаYokoyama, Satoru. "Improving Algorithm for Overlapping Cluster Analysis." In 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.
Повний текст джерелаYokoyama, Satoru, Atsuho Nakayama, and Akinori Okada. "An Application of One-mode Three-way Overlapping Cluster Analysis." In 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.
Повний текст джерелаKim, Paul, and Sangwook Kim. "A Discovery Technique of Overlapping Cluster in Self-Organizing Network." In 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.
Повний текст джерелаТези доповідей конференцій з теми "Overlapping Clusters"
Das, Sunanda, Shreya Chaudhuri, and Asit K. Das. "Cluster analysis for overlapping clusters using genetic algorithm." In 2016 Second International Conference on Research in Computational Intelligence and Communication Networks (ICRCICN). IEEE, 2016. http://dx.doi.org/10.1109/icrcicn.2016.7813542.
Повний текст джерелаtava, Martin, and Pavel Tvrdík. "Overlapping Non-dedicated Clusters Architecture." In 2009 International Conference on Computer Engineering and Technology (ICCET 2009). IEEE, 2009. http://dx.doi.org/10.1109/iccet.2009.66.
Повний текст джерелаAndersen, Reid, David F. Gleich, and Vahab Mirrokni. "Overlapping clusters for distributed computation." In the fifth ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2124295.2124330.
Повний текст джерелаWu, You, Xiong Wang, Zhe Yang, Xiaoying Gan, Xiaohua Tian, and Xinbing Wang. "Crowdclustering items into overlapping clusters." In ICC 2016 - 2016 IEEE International Conference on Communications. IEEE, 2016. http://dx.doi.org/10.1109/icc.2016.7511257.
Повний текст джерелаAydin, Nevin, Farid Nait-Abdesselam, Volodymyr Pryyma, and Damla Turgut. "Overlapping Clusters Algorithm in Ad Hoc Networks." In GLOBECOM 2010 - 2010 IEEE Global Communications Conference. IEEE, 2010. http://dx.doi.org/10.1109/glocom.2010.5683454.
Повний текст джерелаGoldberg, Mark K., Mykola Hayvanovych, and Malik Magdon-Ismail. "Measuring Similarity between Sets of Overlapping Clusters." In 2010 IEEE Second International Conference on Social Computing (SocialCom). IEEE, 2010. http://dx.doi.org/10.1109/socialcom.2010.50.
Повний текст джерелаHe, Xiao, Jing Feng, Bettina Konte, Son T. Mai, and Claudia Plant. "Relevant overlapping subspace clusters on categorical data." In 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.
Повний текст джерелаStava, Martin, and Pavel Tvrdik. "Security System for Overlapping Non-dedicated Clusters." In 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications. IEEE, 2009. http://dx.doi.org/10.1109/ispa.2009.19.
Повний текст джерелаRipon, Kazi Shah Nawaz, and M. N. H. Siddique. "Evolutionary multi-objective clustering for overlapping clusters detection." In 2009 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2009. http://dx.doi.org/10.1109/cec.2009.4983051.
Повний текст джерелаIvannikova, Elena, Anna V. Kononova, and Timo Hamalainen. "Probabilistic group dependence approach for discovering overlapping clusters." In 2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). IEEE, 2016. http://dx.doi.org/10.1109/mlsp.2016.7738882.
Повний текст джерела