Literatura académica sobre el tema "Ant Colony Clustering"
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Artículos de revistas sobre el tema "Ant Colony Clustering"
Gao, Wei. "Improved Ant Colony Clustering Algorithm and Its Performance Study". Computational Intelligence and Neuroscience 2016 (2016): 1–14. http://dx.doi.org/10.1155/2016/4835932.
Texto completoWei, Li Feng. "Design and Implementation of Airline Customer Segmentation System Based on Ant Colony Clustering Algorithm". Advanced Materials Research 433-440 (enero de 2012): 3357–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3357.
Texto completoShelokar, P. S., V. K. Jayaraman y B. D. Kulkarni. "An ant colony approach for clustering". Analytica Chimica Acta 509, n.º 2 (mayo de 2004): 187–95. http://dx.doi.org/10.1016/j.aca.2003.12.032.
Texto completoİnkaya, Tülin, Sinan Kayalıgil y Nur Evin Özdemirel. "Ant Colony Optimization based clustering methodology". Applied Soft Computing 28 (marzo de 2015): 301–11. http://dx.doi.org/10.1016/j.asoc.2014.11.060.
Texto completoGirsang, Abba Suganda, Tjeng Wawan Cenggoro y Ko-Wei Huang. "Fast Ant Colony Optimization for Clustering". Indonesian Journal of Electrical Engineering and Computer Science 12, n.º 1 (1 de octubre de 2018): 78. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp78-86.
Texto completoRunkler, Thomas A. "Ant colony optimization of clustering models". International Journal of Intelligent Systems 20, n.º 12 (2005): 1233–51. http://dx.doi.org/10.1002/int.20111.
Texto completoHu, Kai Hua, Bing Xiang Liu y Yu Jing Zhang. "Ant Colony Clustering Algorithm for Handwritten Arabic Numeral Recognition". Applied Mechanics and Materials 190-191 (julio de 2012): 261–64. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.261.
Texto completoMesleh, Abdelwadood. "Battery Power Clustering Using Ant Colony Optimization". International Journal on Communications Antenna and Propagation (IRECAP) 8, n.º 1 (28 de febrero de 2018): 62. http://dx.doi.org/10.15866/irecap.v8i1.13838.
Texto completoGrace Mercy, M., A. Kamala Kumari, A. Bhujangarao y V. Nooka Raju. "Ant Colony Optimization Algorithm GPS Clustering Approach". Journal of Physics: Conference Series 2040, n.º 1 (1 de octubre de 2021): 012011. http://dx.doi.org/10.1088/1742-6596/2040/1/012011.
Texto completoKlapka, Ondrej y Adrea Vokalova. "Ant Colony Optimization Method for Graph Clustering". Journal of Engineering and Applied Sciences 14, n.º 6 (5 de octubre de 2019): 9289–93. http://dx.doi.org/10.36478/jeasci.2019.9289.9293.
Texto completoTesis sobre el tema "Ant Colony Clustering"
Gu, Yuhua. "Ant clustering with consensus". [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0002959.
Texto completoLi, Jian. "Ensemble clustering via heuristic optimisation". Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/7510.
Texto completoKanade, Parag M. "Fuzzy ants as a clustering concept". [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000397.
Texto completoAGUIAR, José Domingos Albuquerque. "MCAC - Monte Carlo Ant Colony: um novo algoritmo estocástico de agrupamento de dados". Universidade Federal Rural de Pernambuco, 2008. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/5006.
Texto completoMade available in DSpace on 2016-07-06T19:39:45Z (GMT). No. of bitstreams: 1 Jose Domingos Albuquerque Aguiar.pdf: 818824 bytes, checksum: 7c15525f356ca47ab36ddd8ac61ebd31 (MD5) Previous issue date: 2008-02-29
In this work we present a new data cluster algorithm based on social behavior of ants which applies Monte Carlo simulations in selecting the maximum path length of the ants. We compare the performance of the new method with the popular k-means and another algorithm also inspired by the social ant behavior. For the comparative study we employed three data sets from the real world, three deterministic artificial data sets and two random generated data sets, yielding a total of eight data sets. We find that the new algorithm outperforms the others in all studied cases but one. We also address the issue concerning about the right number of groups in a particular data set. Our results show that the proposed algorithm yields a good estimate for the right number of groups present in the data set.
Esta dissertação apresenta um algoritmo inédito de agrupamento de dados que têm como fundamentos o método de Monte Carlo e uma heurística que se baseia no comportamento social das formigas, conhecida como Otimização por Colônias de Formigas. Neste trabalho realizou-se um estudo comparativo do novo algoritmo com outros dois algoritmos de agrupamentos de dados. O primeiro algoritmo é o KMédias que é muito conhecido entre os pesquisadores. O segundo é um algoritmo que utiliza a Otimização por Colônias de Formigas juntamente com um híbrido de outros métodos de otimização. Para implementação desse estudo comparativo utilizaram-se oito conjuntos de dados sendo três conjuntos de dados reais, dois artificiais gerados deterministicamente e três artificiais gerados aleatoriamente. Os resultados do estudo comparativo demonstram que o novo algoritmo identifica padrões nas massas de dados, com desempenho igual ou superior aos outros dois algoritmos avaliados. Neste trabalho investigou-se também a capacidade do novo algoritmo em identificar o número de grupos existentes nos conjuntos dados. Os resultados dessa investigação mostram que o novo algoritmo é capaz de identificar o de número provável de grupos existentes dentro do conjunto de dados.
Inkaya, Tulin. "A Methodology Of Swarm Intelligence Application In Clustering Based On Neighborhood Construction". Phd thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613232/index.pdf.
Texto completoAbidoye, Ademola Philip. "Energy optimization for wireless sensor networks using hierarchical routing techniques". University of the Western Cape, 2015. http://hdl.handle.net/11394/7064.
Texto completoWireless sensor networks (WSNs) have become a popular research area that is widely gaining the attraction from both the research and the practitioner communities due to their wide area of applications. These applications include real-time sensing for audio delivery, imaging, video streaming, and remote monitoring with positive impact in many fields such as precision agriculture, ubiquitous healthcare, environment protection, smart cities and many other fields. While WSNs are aimed to constantly handle more intricate functions such as intelligent computation, automatic transmissions, and in-network processing, such capabilities are constrained by their limited processing capability and memory footprint as well as the need for the sensor batteries to be cautiously consumed in order to extend their lifetime. This thesis revisits the issue of the energy efficiency in sensor networks by proposing a novel clustering approach for routing the sensor readings in wireless sensor networks. The main contribution of this dissertation is to 1) propose corrective measures to the traditional energy model adopted in current sensor networks simulations that erroneously discount both the role played by each node, the sensor node capability and fabric and 2) apply these measures to a novel hierarchical routing architecture aiming at maximizing sensor networks lifetime. We propose three energy models for sensor network: a) a service-aware model that account for the specific role played by each node in a sensor network b) a sensor-aware model and c) load-balancing energy model that accounts for the sensor node fabric and its energy footprint. These two models are complemented by a load balancing model structured to balance energy consumption on the network of cluster heads that forms the backbone for any cluster-based hierarchical sensor network. We present two novel approaches for clustering the nodes of a hierarchical sensor network: a) a distanceaware clustering where nodes are clustered based on their distance and the residual energy and b) a service-aware clustering where the nodes of a sensor network are clustered according to their service offered to the network and their residual energy. These approaches are implemented into a family of routing protocols referred to as EOCIT (Energy Optimization using Clustering Techniques) which combines sensor node energy location and service awareness to achieve good network performance. Finally, building upon the Ant Colony Optimization System (ACS), Multipath Routing protocol based on Ant Colony Optimization approach for Wireless Sensor Networks (MRACO) is proposed as a novel multipath routing protocol that finds energy efficient routing paths for sensor readings dissemination from the cluster heads to the sink/base station of a hierarchical sensor network. Our simulation results reveal the relative efficiency of the newly proposed approaches compared to selected related routing protocols in terms of sensor network lifetime maximization.
Lazic, Jasmina. "New variants of variable neighbourhood search for 0-1 mixed integer programming and clustering". Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/4602.
Texto completoRicciardelli, Elena. "Semi-analytical models of galaxy formation and comparison with observations". Doctoral thesis, Università degli studi di Padova, 2008. http://hdl.handle.net/11577/3426002.
Texto completoJanse, Van Vuuren Michaella. "Human Pose and Action Recognition using Negative Space Analysis". Diss., University of Cape Town, 2004. http://hdl.handle.net/10919/71571.
Texto completoChou, Shin-Chang y 周世章. "Ant Colony System Based Clustering Algorithms". Thesis, 2004. http://ndltd.ncl.edu.tw/handle/01897124838415516873.
Texto completo逢甲大學
交通工程與管理所
92
Cluster analysis is a traditional method of multivariate statistic classification. Cluster analysis is mainly to group all objects into several mutually exclusive clusters in order to make the degree of homogeneity within cluster and the degree of heterogeneity among clusters as high as possible. Cluster analysis is widely applied to many fields, such as pattern recognition, data analysis, image processing and market research. However, Cluster analysis is rapidly becoming computationally intractable as problem scale increases, because of the combinatorial character of the method. It has been proven that cluster analysis becomes an NP-hard problem when the number of clusters exceeds 3. Even the best algorithms developed for some specific objective functions, exhibit complexities of O(N3logN) or O(N3), leaving much room for improvement. Therefore, lots of heuristic algorithms have been proposed for cluster analysis. The performance of ant colony system developed by Dorigo et al. in 1996 based on the behaviors of nature ants out-searching for food has been proven in solving NP-hard and NP-complete combinatorial optimization problems, such as traveling salesman problem, vehicle routing problem, and quadratic assignment problem. This study attempts to propose and validate a clustering algorithm based on ant colony system, which is called ant-based clustering algorithm (ACA). For validating the performance of proposed algorithm in different scale of problems, three different scales of two-dimension data sets have been produced randomly, including small scale (10 samples), medium scale (50 samples) and large scale (100 samples). The comparison is also conducted by comparing its performance with that of agglomerative method, k-means method, and genetic clustering algorithm (GCA). In small scale problem, in addition to agglomerative method, all other three clustering algorithms can solve the optimum solution which is solved by the total enumeration method. In the medium and large scale problems with different number of clusters (3, 5, 7, 9 clusters), ACA statistically significantly outperforms than any other algorithms by 1.04%∼53.42%. GCA performs better than two statistic cluster analysis methods and agglomerative method have worst performance. However, no remarkable difference in the robustness, represented by standard error, has been observed for these four methods. In the case study, a total of 100 accident records data sets have been selected and 6 clustering variables which have significant influence on determining accident responsibility are selected by chi-square test. The results show that ACA still have the best performance in clustering this accidents data into 3 and 5 clusters.
Capítulos de libros sobre el tema "Ant Colony Clustering"
Trejos, Javier, Alex Murillo y Eduardo Piza. "Clustering by Ant Colony Optimization". En Classification, Clustering, and Data Mining Applications, 25–32. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-642-17103-1_3.
Texto completoSchockaert, Steven, Martine De Cock, Chris Cornelis y Etienne E. Kerre. "Fuzzy Ant Based Clustering". En Ant Colony Optimization and Swarm Intelligence, 342–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28646-2_33.
Texto completoTao, Weian, Yan Ma, Jinghua Tian, Mingyong Li, Wenshu Duan y Yuanyuan Liang. "An Improved Ant Colony Clustering Algorithm". En Lecture Notes in Electrical Engineering, 1515–21. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_204.
Texto completoParvin, Hamid y Akram Beigi. "Clustering Ensemble Framework via Ant Colony". En Advances in Soft Computing, 153–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25330-0_14.
Texto completoKao, Yucheng y Kevin Cheng. "An ACO-Based Clustering Algorithm". En Ant Colony Optimization and Swarm Intelligence, 340–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11839088_31.
Texto completoHerrmann, Lutz y Alfred Ultsch. "Strengths and Weaknesses of Ant Colony Clustering". En Advances in Data Analysis, Data Handling and Business Intelligence, 147–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01044-6_13.
Texto completoSuomalainen, Päivi. "Clustering Moodle Data via Ant Colony Optimization". En Lecture Notes in Computer Science, 340–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-32650-9_36.
Texto completoChu, Shu-Chuan, John F. Roddick, Che-Jen Su y Jeng-Shyang Pan. "Constrained Ant Colony Optimization for Data Clustering". En PRICAI 2004: Trends in Artificial Intelligence, 534–43. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28633-2_57.
Texto completoLee, Heesang, Gyuseok Shim, Yun Bae Kim, Jinsoo Park y Jaebum Kim. "A Search Ant and Labor Ant Algorithm for Clustering Data". En Ant Colony Optimization and Swarm Intelligence, 500–501. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11839088_51.
Texto completoAzzag, Hanene, Christiane Guinot y Gilles Venturini. "How to Use Ants for Hierarchical Clustering". En Ant Colony Optimization and Swarm Intelligence, 350–57. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-28646-2_34.
Texto completoActas de conferencias sobre el tema "Ant Colony Clustering"
XianMin Wei. "Clustering processing ant colony algorithm". En 2010 Second Pacific-Asia Conference on Circuits,Communications and System (PACCS). IEEE, 2010. http://dx.doi.org/10.1109/paccs.2010.5626990.
Texto completoZhao, Bao-Jiang. "An Ant Colony Clustering Algorithm". En 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370833.
Texto completoNagarajan, E., Keshetty Saritha y G. MadhuGayathri. "Document clustering using ant colony algorithm". En 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE, 2017. http://dx.doi.org/10.1109/icbdaci.2017.8070884.
Texto completoSadeghi, Zahra y Mohammad Teshnehlab. "Ant colony clustering by expert ants". En 2008 11th International Conference on Computer and Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccitechn.2008.4803115.
Texto completoJiang, Hong, Qingsong Yu y Yu Gong. "An improved ant colony clustering algorithm". En 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2010. http://dx.doi.org/10.1109/bmei.2010.5639719.
Texto completoLiang, Chen. "An Ant Colony Algorithm for Text Clustering". En 2010 International Conference on Computing, Control and Industrial Engineering. IEEE, 2010. http://dx.doi.org/10.1109/ccie.2010.180.
Texto completoWang, Yong, Wei Zhang, Jun Chen, Jianfu Li y Li Xiao. "Using ant colony optimization for efficient clustering". En International Workshop and Conference on Photonics and Nanotechnology 2007, editado por Minoru Sasaki, Gisang Choi Sang, Zushu Li, Ryojun Ikeura, Hyungki Kim y Fangzheng Xue. SPIE, 2007. http://dx.doi.org/10.1117/12.784045.
Texto completoJie, Shen, He Kun, Wei Liu-hua, Bi Lei, Sun Rong-shuang y Xu Fa-yan. "State Information-based Ant Colony Clustering Algorithm". En 2008 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2008. http://dx.doi.org/10.1109/icnsc.2008.4525294.
Texto completoFu, Hui. "A Novel Clustering Algorithm with Ant Colony Optimization". En 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA). IEEE, 2008. http://dx.doi.org/10.1109/paciia.2008.75.
Texto completoLi, Jinjiang, Hui Fan, Da Yuan y Caiming Zhang. "Kernel Function Clustering Based on Ant Colony Algorithm". En 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.232.
Texto completoInformes sobre el tema "Ant Colony Clustering"
Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak y Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, julio de 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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