Relevant bibliographies by topics / Ant Colony Clustering

Academic literature on the topic 'Ant Colony Clustering'

Author: Grafiati
Published: 14 March 2023

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Contents

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Ant Colony Clustering.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Ant Colony Clustering"

1

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.

Full text
Abstract:
Clustering analysis is used in many disciplines and applications; it is an important tool that descriptively identifies homogeneous groups of objects based on attribute values. The ant colony clustering algorithm is a swarm-intelligent method used for clustering problems that is inspired by the behavior of ant colonies that cluster their corpses and sort their larvae. A new abstraction ant colony clustering algorithm using a data combination mechanism is proposed to improve the computational efficiency and accuracy of the ant colony clustering algorithm. The abstraction ant colony clustering algorithm is used to cluster benchmark problems, and its performance is compared with the ant colony clustering algorithm and other methods used in existing literature. Based on similar computational difficulties and complexities, the results show that the abstraction ant colony clustering algorithm produces results that are not only more accurate but also more efficiently determined than the ant colony clustering algorithm and the other methods. Thus, the abstraction ant colony clustering algorithm can be used for efficient multivariate data clustering.
APA, Harvard, Vancouver, ISO, and other styles
2

Wei, Li Feng. "Design and Implementation of Airline Customer Segmentation System Based on Ant Colony Clustering Algorithm." Advanced Materials Research 433-440 (January 2012): 3357–61. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.3357.

Full text
Abstract:
Segmentation based on customer value and needs can better guide marketing decision-making of airlines as well as better understand needs of high-value passengers. To address customer segmentation in Customer Relationship Management (CRM), the paper proposed and designed airline customer segmentation system structure based on ant colony clustering. The key ant colony clustering algorithm was also designed and implemented. The ant colony clustering algorithm mainly used adaptively adjusted group similarity to perform clustering and access to initial clustering result. Then all data representation points and abnormal data were inputted into lattice plane scattered randomly. Ant colony algorithm was used for clustering once again and corresponding class label was used to delete abnormal values and obtain complete clusters. Data test example based on ant colony clustering customer analysis platform illustrated its feasibility and effectiveness
APA, Harvard, Vancouver, ISO, and other styles
3

Shelokar, P. S., V. K. Jayaraman, and B. D. Kulkarni. "An ant colony approach for clustering." Analytica Chimica Acta 509, no. 2 (May 2004): 187–95. http://dx.doi.org/10.1016/j.aca.2003.12.032.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

İnkaya, Tülin, Sinan Kayalıgil, and Nur Evin Özdemirel. "Ant Colony Optimization based clustering methodology." Applied Soft Computing 28 (March 2015): 301–11. http://dx.doi.org/10.1016/j.asoc.2014.11.060.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Girsang, Abba Suganda, Tjeng Wawan Cenggoro, and Ko-Wei Huang. "Fast Ant Colony Optimization for Clustering." Indonesian Journal of Electrical Engineering and Computer Science 12, no. 1 (October 1, 2018): 78. http://dx.doi.org/10.11591/ijeecs.v12.i1.pp78-86.

Full text
Abstract:
<p>Data clustering is popular data analysis approaches, which used to organizing data into sensible clusters based on similarity measure, where data within a cluster are similar to each other but dissimilar to that of another cluster. In the recently, the cluster problem has been proven as NP-hard problem, thus, it can be solved with meta-heuristic algorithms, such as the particle swarm optimization (PSO), genetic algorithm (GA), and ant colony optimization (ACO), respectively. This paper proposes an algorithm called Fast Ant Colony Optimization for Clustering (FACOC) to reduce the computation time of Ant Colony Optimization (ACO) in clustering problem. FACOC is developed by the motivation that a redundant computation is occurred in ACO for clustering. This redundant computation can be cut in order to reduce the computation time of ACO for clustering. The proposed FACOC algorithm was verified on 5 well-known benchmarks. Experimental result shows that by cutting this redundant computation, the computation time can be reduced about 28% while only suffering a small quality degradation.</p>
APA, Harvard, Vancouver, ISO, and other styles
6

Runkler, Thomas A. "Ant colony optimization of clustering models." International Journal of Intelligent Systems 20, no. 12 (2005): 1233–51. http://dx.doi.org/10.1002/int.20111.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Hu, Kai Hua, Bing Xiang Liu, and Yu Jing Zhang. "Ant Colony Clustering Algorithm for Handwritten Arabic Numeral Recognition." Applied Mechanics and Materials 190-191 (July 2012): 261–64. http://dx.doi.org/10.4028/www.scientific.net/amm.190-191.261.

Full text
Abstract:
This article is based on Ant Colony Clustering Algorithm for Handwritten Arabic Numeral Recognition on an image. Under the precondition of the clustering numbers are known and a adequate treatment process was developed for the image, we carry through cluster analysis to digital image by experiment. The article elaborates on the basic concepts and the algorithm’s principle of Ant Colony Clustering Algorithm. The workflow to the algorithmic flow of Ant Colony Clustering Algorithm will be elaborated in the following chapters. The paper also detailed discuss the implement of the algorithm.
APA, Harvard, Vancouver, ISO, and other styles
8

Mesleh, Abdelwadood. "Battery Power Clustering Using Ant Colony Optimization." International Journal on Communications Antenna and Propagation (IRECAP) 8, no. 1 (February 28, 2018): 62. http://dx.doi.org/10.15866/irecap.v8i1.13838.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Grace Mercy, M., A. Kamala Kumari, A. Bhujangarao, and V. Nooka Raju. "Ant Colony Optimization Algorithm GPS Clustering Approach." Journal of Physics: Conference Series 2040, no. 1 (October 1, 2021): 012011. http://dx.doi.org/10.1088/1742-6596/2040/1/012011.

Full text
Abstract:
Abstract The geometry of the GPS satellite recipient (s), which reflects the recipient (s) of the satellites, has a major influence on the total positioning precision. The more precise the position, the stronger the geometry of the satellite. This article provides the geometry of satellite clustering for the selection of suitable satellite navigation subsets. This technique is based on the GDOP (Geometric Precision Dilution) satellite factor cluster with the Ant Colony Optimization (ACO) algorithm that has been created by simulating real and artificial ways to locate the quickest route between nesting resources and food. Pheromones are utilised in the suggested technique to assess the iterative outcome of single colonies. The ACO method can measure all subsets of satellites while reducing computer load by eliminating the need for a matrix inversion. Based on the simulation results, the GPS GDOP clustering technique is more efficient at achieving its optimum value.
APA, Harvard, Vancouver, ISO, and other styles
10

Klapka, Ondrej, and Adrea Vokalova. "Ant Colony Optimization Method for Graph Clustering." Journal of Engineering and Applied Sciences 14, no. 6 (October 5, 2019): 9289–93. http://dx.doi.org/10.36478/jeasci.2019.9289.9293.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Ant Colony Clustering"

1

Gu, Yuhua. "Ant clustering with consensus." [Tampa, Fla] : University of South Florida, 2009. http://purl.fcla.edu/usf/dc/et/SFE0002959.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Li, Jian. "Ensemble clustering via heuristic optimisation." Thesis, Brunel University, 2010. http://bura.brunel.ac.uk/handle/2438/7510.

Full text
Abstract:
Traditional clustering algorithms have different criteria and biases, and there is no single algorithm that can be the best solution for a wide range of data sets. This problem often presents a significant obstacle to analysts in revealing meaningful information buried among the huge amount of data. Ensemble Clustering has been proposed as a way to avoid the biases and improve the accuracy of clustering. The difficulty in developing Ensemble Clustering methods is to combine external information (provided by input clusterings) with internal information (i.e. characteristics of given data) effectively to improve the accuracy of clustering. The work presented in this thesis focuses on enhancing the clustering accuracy of Ensemble Clustering by employing heuristic optimisation techniques to achieve a robust combination of relevant information during the consensus clustering stage. Two novel heuristic optimisation-based Ensemble Clustering methods, Multi-Optimisation Consensus Clustering (MOCC) and K-Ants Consensus Clustering (KACC), are developed and introduced in this thesis. These methods utilise two heuristic optimisation algorithms (Simulated Annealing and Ant Colony Optimisation) for their Ensemble Clustering frameworks, and have been proved to outperform other methods in the area. The extensive experimental results, together with a detailed analysis, will be presented in this thesis.
APA, Harvard, Vancouver, ISO, and other styles
3

Kanade, Parag M. "Fuzzy ants as a clustering concept." [Tampa, Fla.] : University of South Florida, 2004. http://purl.fcla.edu/fcla/etd/SFE0000397.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

AGUIAR, 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.

Full text
Abstract:
Submitted by (ana.araujo@ufrpe.br) on 2016-07-06T19:39:45Z No. of bitstreams: 1 Jose Domingos Albuquerque Aguiar.pdf: 818824 bytes, checksum: 7c15525f356ca47ab36ddd8ac61ebd31 (MD5)
Made 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.
APA, Harvard, Vancouver, ISO, and other styles
5

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.

Full text
Abstract:
In this dissertation, we consider the clustering problem in data sets with unknown number of clusters having arbitrary shapes, intracluster and intercluster density variations. We introduce a clustering methodology which is composed of three methods that ensures extraction of local density and connectivity properties, data set reduction, and clustering. The first method constructs a unique neighborhood for each data point using the connectivity and density relations among the points based upon the graph theoretical concepts, mainly Gabriel Graphs. Neighborhoods subsequently connected form subclusters (closures) which constitute the skeleton of the clusters. In the second method, the external shape concept in computational geometry is adapted for data set reduction and cluster visualization. This method extracts the external shape of a non-convex n-dimensional data set using Delaunay triangulation. In the third method, we inquire the applicability of Swarm Intelligence to clustering using Ant Colony Optimization (ACO). Ants explore the data set so that the clusters are detected using density break-offs, connectivity and distance information. The proposed ACO-based algorithm uses the outputs of the neighborhood construction (NC) and the external shape formation. In addition, we propose a three-phase clustering algorithm that consists of NC, outlier detection and merging phases. We test the strengths and the weaknesses of the proposed approaches by extensive experimentation with data sets borrowed from literature and generated in a controlled manner. NC is found to be effective for arbitrary shaped clusters, intracluster and intercluster density variations. The external shape formation algorithm achieves significant reductions for convex clusters. The ACO-based and the three-phase clustering algorithms have promising results for the data sets having well-separated clusters.
APA, Harvard, Vancouver, ISO, and other styles
6

Abidoye, Ademola Philip. "Energy optimization for wireless sensor networks using hierarchical routing techniques." University of the Western Cape, 2015. http://hdl.handle.net/11394/7064.

Full text
Abstract:
Philosophiae Doctor - PhD
Wireless 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.
APA, Harvard, Vancouver, ISO, and other styles
7

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.

Full text
Abstract:
Many real-world optimisation problems are discrete in nature. Although recent rapid developments in computer technologies are steadily increasing the speed of computations, the size of an instance of a hard discrete optimisation problem solvable in prescribed time does not increase linearly with the computer speed. This calls for the development of new solution methodologies for solving larger instances in shorter time. Furthermore, large instances of discrete optimisation problems are normally impossible to solve to optimality within a reasonable computational time/space and can only be tackled with a heuristic approach. In this thesis the development of so called matheuristics, the heuristics which are based on the mathematical formulation of the problem, is studied and employed within the variable neighbourhood search framework. Some new variants of the variable neighbourhood searchmetaheuristic itself are suggested, which naturally emerge from exploiting the information from the mathematical programming formulation of the problem. However, those variants may also be applied to problems described by the combinatorial formulation. A unifying perspective on modern advances in local search-based metaheuristics, a so called hyper-reactive approach, is also proposed. Two NP-hard discrete optimisation problems are considered: 0-1 mixed integer programming and clustering with application to colour image quantisation. Several new heuristics for 0-1 mixed integer programming problem are developed, based on the principle of variable neighbourhood search. One set of proposed heuristics consists of improvement heuristics, which attempt to find high-quality near-optimal solutions starting from a given feasible solution. Another set consists of constructive heuristics, which attempt to find initial feasible solutions for 0-1 mixed integer programs. Finally, some variable neighbourhood search based clustering techniques are applied for solving the colour image quantisation problem. All new methods presented are compared to other algorithms recommended in literature and a comprehensive performance analysis is provided. Computational results show that the methods proposed either outperform the existing state-of-the-art methods for the problems observed, or provide comparable results. The theory and algorithms presented in this thesis indicate that hybridisation of the CPLEX MIP solver and the VNS metaheuristic can be very effective for solving large instances of the 0-1 mixed integer programming problem. More generally, the results presented in this thesis suggest that hybridisation of exact (commercial) integer programming solvers and some metaheuristic methods is of high interest and such combinations deserve further practical and theoretical investigation. Results also show that VNS can be successfully applied to solving a colour image quantisation problem.
APA, Harvard, Vancouver, ISO, and other styles
8

Ricciardelli, 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.

Full text
Abstract:
In this Thesis we attempted to answer to some of the fundamental questions concerning galaxy evolution. In particular when and how galaxies got their present-day stellar content and how this process depends on their mass. In order to address this key issues, we developed a new semi-analytic model of galaxy formation (GECO, Galaxy Evolution COde), that couples a Monte Carlo representation of the hierarchical clustering of dark matter haloes with analytic recipes for the baryonic physics, such as the cooling of the gas, the star formation, the feedback from SN and AGN. We set the model on observations in the local universe and then we predict and compare results for the high redshift galaxies.
APA, Harvard, Vancouver, ISO, and other styles
9

Janse, Van Vuuren Michaella. "Human Pose and Action Recognition using Negative Space Analysis." Diss., University of Cape Town, 2004. http://hdl.handle.net/10919/71571.

Full text
Abstract:
This thesis proposes a novel approach to extracting pose information from image sequences. Current state of the art techniques focus exclusively on the image space occupied by the body for pose and action recognition. The method proposed here, however, focuses on the negative spaces: the areas surrounding the individual. This has resulted in the colour-coded negative space approach, an image preprocessing step that circumvents the need for complicated model fitting or template matching methods. The approach can be described as follows: negative spaces surrounding the human silhouette are extracted using horizontal and vertical scanning processes. These negative space areas are more numerous, and undergo more radical changes in shape than the single area occupied by the figure of the person performing an action. The colour-coded negative space representation is formed using the four binary images produced by the scanning processes. Features are then extracted from the colour-coded images. These are based on the percentage of area occupied by distinct coloured regions as well as the bounding box proportions. Pose clusters are identified using feedback from an independent action set. Subsequent images are classified using a simple Euclidean distance measure. An image sequence is thus temporally segmented into its corresponding pose representations. Action recognition simply becomes the detection of a temporally ordered sequence of poses that characterises the action. The method is purely vision-based, utilising monocular images with no need for body markers or special clothing. Two datasets were constructed using several actors performing different poses and actions. Some of these actions included actors waving their arms, sitting down or kicking a leg. These actions were recorded against a monochrome background to simplify the segmentation of the actors from the background. The actions were then recorded on DV cam and digitised into a data base. The silhouette images from these actions were isolated and placed in a frame or bounding box. The next step was to highlight the negative spaces using a directional scanning method. This scanning method colour-codes the negative spaces of each action. What became immediately apparent is that very distinctive colour patterns formed for different actions. To emphasise the action, different colours were allocated to negative spaces surrounding the image. For example, the space between the legs of an actor standing in a T - pose with legs apart would be allocated yellow, while the space below the arms were allocated different shades of green. The space surrounding the head would be different shades of purple. During an action when the actor moves one leg up in a kicking fashion, the yellow colour would increase. Inversely, when the actor closes his legs and puts them together, the yellow colour filling the negative space would decrease substantially. What also became apparent is that these coloured negative spaces are interdependent and that they influence each other during the course of an action. For example, when an actor lifts one of his legs, increasing the yellow-coded negative space, the green space between that leg and the arm decreases. This interrelationship between colours hold true for all poses and actions as presented in this thesis. In terms of pose recognition, it is significant that these colour coded negative spaces and the way the change during an action or a movement are substantial and instantly recognisable. Compare for example, looking at someone lifting an arm as opposed to seeing a vast negative space changing shape. In a controlled research environment, several actors were instructed to perform a number of different actions. After colour coding the negative spaces, it became apparent that every action can be recognised by a unique colour coded pattern. The challenge is to ascribe a numerical presentation, a mathematical quotation, to extract the essence of what is so visually apparent. The essence of pose recognition and it's measurability lies in the relationship between the colours in these negative spaces and how they impact on each other during a pose or an action. The simplest way of measuring this relationship is by calculating the percentage of each colour present during an action. These calculated percentages become the basis of pose and action recognition. By plotting these percentages on a graph confirms that the essence of these different actions and poses can in fact been captured and recognised. Despite variations in these traces caused by time differences, personal appearance and mannerisms, what emerged is a clear recognisable pattern that can be married to an action or different parts of an action. 7 Actors might lift their left leg, some slightly higher than others, some slower than others and these variations in terms of colour percentages would be recorded as a trace, but there would be very specific stages during the action where the traces would correspond, making the action recognisable.In conclusion, using negative space as a tool in human pose and tracking recognition presents an exiting research avenue because it is influenced less by variations such as difference in personal appearance and changes in the angle of observation. This approach is also simplistic and does not rely on complicated models and templates
APA, Harvard, Vancouver, ISO, and other styles
10

Chou, Shin-Chang, and 周世章. "Ant Colony System Based Clustering Algorithms." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/01897124838415516873.

Full text
Abstract:
碩士
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.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Ant Colony Clustering"

1

Trejos, Javier, Alex Murillo, and Eduardo Piza. "Clustering by Ant Colony Optimization." In 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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Schockaert, Steven, Martine De Cock, Chris Cornelis, and Etienne E. Kerre. "Fuzzy Ant Based Clustering." In 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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Tao, Weian, Yan Ma, Jinghua Tian, Mingyong Li, Wenshu Duan, and Yuanyuan Liang. "An Improved Ant Colony Clustering Algorithm." In Lecture Notes in Electrical Engineering, 1515–21. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-2386-6_204.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Parvin, Hamid, and Akram Beigi. "Clustering Ensemble Framework via Ant Colony." In Advances in Soft Computing, 153–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25330-0_14.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Kao, Yucheng, and Kevin Cheng. "An ACO-Based Clustering Algorithm." In Ant Colony Optimization and Swarm Intelligence, 340–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11839088_31.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Herrmann, Lutz, and Alfred Ultsch. "Strengths and Weaknesses of Ant Colony Clustering." In 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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Suomalainen, Päivi. "Clustering Moodle Data via Ant Colony Optimization." In 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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Chu, Shu-Chuan, John F. Roddick, Che-Jen Su, and Jeng-Shyang Pan. "Constrained Ant Colony Optimization for Data Clustering." In 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.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Lee, Heesang, Gyuseok Shim, Yun Bae Kim, Jinsoo Park, and Jaebum Kim. "A Search Ant and Labor Ant Algorithm for Clustering Data." In Ant Colony Optimization and Swarm Intelligence, 500–501. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11839088_51.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Azzag, Hanene, Christiane Guinot, and Gilles Venturini. "How to Use Ants for Hierarchical Clustering." In 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.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Ant Colony Clustering"

1

XianMin Wei. "Clustering processing ant colony algorithm." In 2010 Second Pacific-Asia Conference on Circuits,Communications and System (PACCS). IEEE, 2010. http://dx.doi.org/10.1109/paccs.2010.5626990.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Zhao, Bao-Jiang. "An Ant Colony Clustering Algorithm." In 2007 International Conference on Machine Learning and Cybernetics. IEEE, 2007. http://dx.doi.org/10.1109/icmlc.2007.4370833.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Nagarajan, E., Keshetty Saritha, and G. MadhuGayathri. "Document clustering using ant colony algorithm." In 2017 International Conference on Big Data Analytics and Computational Intelligence (ICBDAC). IEEE, 2017. http://dx.doi.org/10.1109/icbdaci.2017.8070884.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Sadeghi, Zahra, and Mohammad Teshnehlab. "Ant colony clustering by expert ants." In 2008 11th International Conference on Computer and Information Technology (ICCIT). IEEE, 2008. http://dx.doi.org/10.1109/iccitechn.2008.4803115.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Jiang, Hong, Qingsong Yu, and Yu Gong. "An improved ant colony clustering algorithm." In 2010 3rd International Conference on Biomedical Engineering and Informatics (BMEI). IEEE, 2010. http://dx.doi.org/10.1109/bmei.2010.5639719.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Liang, Chen. "An Ant Colony Algorithm for Text Clustering." In 2010 International Conference on Computing, Control and Industrial Engineering. IEEE, 2010. http://dx.doi.org/10.1109/ccie.2010.180.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Wang, Yong, Wei Zhang, Jun Chen, Jianfu Li, and Li Xiao. "Using ant colony optimization for efficient clustering." In International Workshop and Conference on Photonics and Nanotechnology 2007, edited by Minoru Sasaki, Gisang Choi Sang, Zushu Li, Ryojun Ikeura, Hyungki Kim, and Fangzheng Xue. SPIE, 2007. http://dx.doi.org/10.1117/12.784045.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Jie, Shen, He Kun, Wei Liu-hua, Bi Lei, Sun Rong-shuang, and Xu Fa-yan. "State Information-based Ant Colony Clustering Algorithm." In 2008 IEEE International Conference on Networking, Sensing and Control (ICNSC). IEEE, 2008. http://dx.doi.org/10.1109/icnsc.2008.4525294.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Fu, Hui. "A Novel Clustering Algorithm with Ant Colony Optimization." In 2008 Pacific-Asia Workshop on Computational Intelligence and Industrial Application (PACIIA). IEEE, 2008. http://dx.doi.org/10.1109/paciia.2008.75.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Li, Jinjiang, Hui Fan, Da Yuan, and Caiming Zhang. "Kernel Function Clustering Based on Ant Colony Algorithm." In 2008 Fourth International Conference on Natural Computation. IEEE, 2008. http://dx.doi.org/10.1109/icnc.2008.232.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Ant Colony Clustering"

1

Engel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.

Full text
Abstract:
The objectives of this project were to develop procedures and models, based on neural networks, for quality sorting of agricultural produce. Two research teams, one in Purdue University and the other in Israel, coordinated their research efforts on different aspects of each objective utilizing both melons and tomatoes as case studies. At Purdue: An expert system was developed to measure variances in human grading. Data were acquired from eight sensors: vision, two firmness sensors (destructive and nondestructive), chlorophyll from fluorescence, color sensor, electronic sniffer for odor detection, refractometer and a scale (mass). Data were analyzed and provided input for five classification models. Chlorophyll from fluorescence was found to give the best estimation for ripeness stage while the combination of machine vision and firmness from impact performed best for quality sorting. A new algorithm was developed to estimate and minimize training size for supervised classification. A new criteria was established to choose a training set such that a recurrent auto-associative memory neural network is stabilized. Moreover, this method provides for rapid and accurate updating of the classifier over growing seasons, production environments and cultivars. Different classification approaches (parametric and non-parametric) for grading were examined. Statistical methods were found to be as accurate as neural networks in grading. Classification models by voting did not enhance the classification significantly. A hybrid model that incorporated heuristic rules and either a numerical classifier or neural network was found to be superior in classification accuracy with half the required processing of solely the numerical classifier or neural network. In Israel: A multi-sensing approach utilizing non-destructive sensors was developed. Shape, color, stem identification, surface defects and bruises were measured using a color image processing system. Flavor parameters (sugar, acidity, volatiles) and ripeness were measured using a near-infrared system and an electronic sniffer. Mechanical properties were measured using three sensors: drop impact, resonance frequency and cyclic deformation. Classification algorithms for quality sorting of fruit based on multi-sensory data were developed and implemented. The algorithms included a dynamic artificial neural network, a back propagation neural network and multiple linear regression. Results indicated that classification based on multiple sensors may be applied in real-time sorting and can improve overall classification. Advanced image processing algorithms were developed for shape determination, bruise and stem identification and general color and color homogeneity. An unsupervised method was developed to extract necessary vision features. The primary advantage of the algorithms developed is their ability to learn to determine the visual quality of almost any fruit or vegetable with no need for specific modification and no a-priori knowledge. Moreover, since there is no assumption as to the type of blemish to be characterized, the algorithm is capable of distinguishing between stems and bruises. This enables sorting of fruit without knowing the fruits' orientation. A new algorithm for on-line clustering of data was developed. The algorithm's adaptability is designed to overcome some of the difficulties encountered when incrementally clustering sparse data and preserves information even with memory constraints. Large quantities of data (many images) of high dimensionality (due to multiple sensors) and new information arriving incrementally (a function of the temporal dynamics of any natural process) can now be processed. Furhermore, since the learning is done on-line, it can be implemented in real-time. The methodology developed was tested to determine external quality of tomatoes based on visual information. An improved model for color sorting which is stable and does not require recalibration for each season was developed for color determination. Excellent classification results were obtained for both color and firmness classification. Results indicted that maturity classification can be obtained using a drop-impact and a vision sensor in order to predict the storability and marketing of harvested fruits. In conclusion: We have been able to define quantitatively the critical parameters in the quality sorting and grading of both fresh market cantaloupes and tomatoes. We have been able to accomplish this using nondestructive measurements and in a manner consistent with expert human grading and in accordance with market acceptance. This research constructed and used large databases of both commodities, for comparative evaluation and optimization of expert system, statistical and/or neural network models. The models developed in this research were successfully tested, and should be applicable to a wide range of other fruits and vegetables. These findings are valuable for the development of on-line grading and sorting of agricultural produce through the incorporation of multiple measurement inputs that rapidly define quality in an automated manner, and in a manner consistent with the human graders and inspectors.
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Ant Colony Clustering' for particular source types:
Journal articles Dissertations / Theses
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography