Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121655
Hossein Barghijand, A. Akbarimajd, J. Keighobadi
Quasi-static object manipulation using a wheeled mobile robot is studied. For a mobile robot, as a typical set-up for transportation of objects, dynamic grasp problem is defined. It is assumed that the robot moves along a straight path and carries an object on its upper surface and the object should not move relative to the robot (dynamic grasp problem). It is also assumed that the robot has limited wheel motors torque. It is shown that to preserve dynamic grasp between the object and the robot, acceleration of the robot has to remain in a specified range. A third order polynomial for position equation of the mobile robot is selected and dynamic grasp and torque conditions are sketched as a constraint on acceleration of the robot. An optimization problem is proposed to find the optimal motion of the robot. A genetic algorithm is proposed to solve the optimization problem. The solutions are validated by simulations in MSC-ADAMS and MATLAB in general case of the optimization problem and also in minimum time, maximum distance and minimum energy problems as special cases of the general problem.
{"title":"Quasi-static object manipulation by mobile robot: Optimal motion planning using GA","authors":"Hossein Barghijand, A. Akbarimajd, J. Keighobadi","doi":"10.1109/ISDA.2011.6121655","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121655","url":null,"abstract":"Quasi-static object manipulation using a wheeled mobile robot is studied. For a mobile robot, as a typical set-up for transportation of objects, dynamic grasp problem is defined. It is assumed that the robot moves along a straight path and carries an object on its upper surface and the object should not move relative to the robot (dynamic grasp problem). It is also assumed that the robot has limited wheel motors torque. It is shown that to preserve dynamic grasp between the object and the robot, acceleration of the robot has to remain in a specified range. A third order polynomial for position equation of the mobile robot is selected and dynamic grasp and torque conditions are sketched as a constraint on acceleration of the robot. An optimization problem is proposed to find the optimal motion of the robot. A genetic algorithm is proposed to solve the optimization problem. The solutions are validated by simulations in MSC-ADAMS and MATLAB in general case of the optimization problem and also in minimum time, maximum distance and minimum energy problems as special cases of the general problem.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"40 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128582483","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121687
U. F. Siddiqi, Y. Shiraishi, S. M. Sait
Route optimization (RO) is an important feature of the Electric Vehicles (EVs) which is responsible for finding optimized paths between any source and destination nodes in the road network. In this paper, the RO problem of EVs is solved by using the Multi Constrained Optimal Path (MCOP) approach. The proposed MCOP problem aims to minimize the length of the path and meets constraints on total travelling time, total time delay due to signals, total recharging time, and total recharging cost. The Penalty Function method is used to transform the MCOP problem into unconstrained optimization problem. The unconstrained optimization is performed by using a Particle Swarm Optimization (PSO) based algorithm. The proposed algorithm has innovative methods for finding the velocity of the particles and updating their positions. The performance of the proposed algorithm is compared with two previous heuristics: H_MCOP and Genetic Algorithm (GA). The time of optimization is varied between 1 second (s) and 5s. The proposed algorithm has obtained the minimum value of the objective function in at-least 9.375% more test instances than the GA and H_MCOP
{"title":"Multi-constrained route optimization for Electric Vehicles (EVs) using Particle Swarm Optimization (PSO)","authors":"U. F. Siddiqi, Y. Shiraishi, S. M. Sait","doi":"10.1109/ISDA.2011.6121687","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121687","url":null,"abstract":"Route optimization (RO) is an important feature of the Electric Vehicles (EVs) which is responsible for finding optimized paths between any source and destination nodes in the road network. In this paper, the RO problem of EVs is solved by using the Multi Constrained Optimal Path (MCOP) approach. The proposed MCOP problem aims to minimize the length of the path and meets constraints on total travelling time, total time delay due to signals, total recharging time, and total recharging cost. The Penalty Function method is used to transform the MCOP problem into unconstrained optimization problem. The unconstrained optimization is performed by using a Particle Swarm Optimization (PSO) based algorithm. The proposed algorithm has innovative methods for finding the velocity of the particles and updating their positions. The performance of the proposed algorithm is compared with two previous heuristics: H_MCOP and Genetic Algorithm (GA). The time of optimization is varied between 1 second (s) and 5s. The proposed algorithm has obtained the minimum value of the objective function in at-least 9.375% more test instances than the GA and H_MCOP","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129531200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121692
S. Khelifati, F. Benbouzid-Sitayeb
Many works refer to the scheduling problem of both preventive maintenance and production activities. Few works concern the dynamic scheduling problem of these two activities. This aspect is mainly concerned by corrective maintenance activities (equipment failure). In this regard, we propose a distributed approach using multi-agent paradigm for scheduling independent jobs and maintenance operations in the flowshop sequencing problem. The proposed multi-agent system introduces a dialogue between two communities of agents (production and maintenance) based on a two-step sequential strategy: first scheduling the production jobs then inserting the preventive maintenance operations, taking the production schedule as a mandatory constraint, to generate a joint production and maintenance schedule. The objective is then to optimize a bi-objective function which takes into account both maintenance and production criterion. It also provides a framework in order to react to the disturbances occurring in the workshop. The main point is to show how the proposed multi-agent system provides a better compromise between the satisfactions of respective objectives of the two functions.
{"title":"A sequantial distributed approach for the joint scheduling of jobs and maintenance operations in the flowshop sequencing problem","authors":"S. Khelifati, F. Benbouzid-Sitayeb","doi":"10.1109/ISDA.2011.6121692","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121692","url":null,"abstract":"Many works refer to the scheduling problem of both preventive maintenance and production activities. Few works concern the dynamic scheduling problem of these two activities. This aspect is mainly concerned by corrective maintenance activities (equipment failure). In this regard, we propose a distributed approach using multi-agent paradigm for scheduling independent jobs and maintenance operations in the flowshop sequencing problem. The proposed multi-agent system introduces a dialogue between two communities of agents (production and maintenance) based on a two-step sequential strategy: first scheduling the production jobs then inserting the preventive maintenance operations, taking the production schedule as a mandatory constraint, to generate a joint production and maintenance schedule. The objective is then to optimize a bi-objective function which takes into account both maintenance and production criterion. It also provides a framework in order to react to the disturbances occurring in the workshop. The main point is to show how the proposed multi-agent system provides a better compromise between the satisfactions of respective objectives of the two functions.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129683713","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121770
John See
The inherent properties of video sequences allow for representation of data in both spatial and temporal dimensions. Using conventional image-based methods for face recognition in video is often an ineffective approach as the essential spatio-temporal properties are not fully harnessed. This paper proposes a probabilistic Bayesian network classifier to accomplish effective recognition of faces in video sequences. In our model, we introduce a joint probability function that encodes the causal dependencies between video frames, selected exemplars or representative images of a video, and subject classes. This enables both the temporal continuity between video frames and also the spatial relationships between exemplars and their respective exemplar-set classes to be captured. To simplify the tedious estimation of densities, the proposed method also utilizes probabilistic similarity scores that are computationally inexpensive. Good recognition rates were achieved by our proposed method in comprehensive experiments conducted on two standard face video datasets.
{"title":"Probabilistic Bayesian network classifier for face recognition in video sequences","authors":"John See","doi":"10.1109/ISDA.2011.6121770","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121770","url":null,"abstract":"The inherent properties of video sequences allow for representation of data in both spatial and temporal dimensions. Using conventional image-based methods for face recognition in video is often an ineffective approach as the essential spatio-temporal properties are not fully harnessed. This paper proposes a probabilistic Bayesian network classifier to accomplish effective recognition of faces in video sequences. In our model, we introduce a joint probability function that encodes the causal dependencies between video frames, selected exemplars or representative images of a video, and subject classes. This enables both the temporal continuity between video frames and also the spatial relationships between exemplars and their respective exemplar-set classes to be captured. To simplify the tedious estimation of densities, the proposed method also utilizes probabilistic similarity scores that are computationally inexpensive. Good recognition rates were achieved by our proposed method in comprehensive experiments conducted on two standard face video datasets.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"224 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130695598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121806
María Arsuaga-Ríos, M. A. Vega-Rodríguez, F. Castrillo
Often, solutions to complex problems are found in nature. Swarm algorithms are capable of solving such complex problems by implementing patterns from nature. This patterns are found in a variety of scientific fields. In this paper, we discuss two swarm algorithms extracted from Biology and Physics, namely: Multiobjective Artificial Bee Colony (MOABC) and Multiobjective Gravitational Search Algorithm (MOGSA). The first one is based on bees behavior and the other follows the gravity between masses. These algorithms are implemented to solve the grid scheduling problem. Optimization of job scheduling is one of the most challenging tasks in Grid environments because it severely affects the execution time of an experiment (set of jobs). Experiments often are tied up to fulfill deadlines and budgets. One of the main contributions of this work is adding multiobjective processes to these swarm algorithms to minimize those conflictive objectives. Results show that MOABC clearly improves the MOGSA approach when solving the problem. MOABC is also compared with real grid meta-schedulers as Deadline Budget Constraint (DBC) and Workload Management System (WMS) by using the simulator GridSim to prove the improvement that offers this new algorithm.
{"title":"Evaluation of multiobjective swarm algorithms for grid scheduling","authors":"María Arsuaga-Ríos, M. A. Vega-Rodríguez, F. Castrillo","doi":"10.1109/ISDA.2011.6121806","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121806","url":null,"abstract":"Often, solutions to complex problems are found in nature. Swarm algorithms are capable of solving such complex problems by implementing patterns from nature. This patterns are found in a variety of scientific fields. In this paper, we discuss two swarm algorithms extracted from Biology and Physics, namely: Multiobjective Artificial Bee Colony (MOABC) and Multiobjective Gravitational Search Algorithm (MOGSA). The first one is based on bees behavior and the other follows the gravity between masses. These algorithms are implemented to solve the grid scheduling problem. Optimization of job scheduling is one of the most challenging tasks in Grid environments because it severely affects the execution time of an experiment (set of jobs). Experiments often are tied up to fulfill deadlines and budgets. One of the main contributions of this work is adding multiobjective processes to these swarm algorithms to minimize those conflictive objectives. Results show that MOABC clearly improves the MOGSA approach when solving the problem. MOABC is also compared with real grid meta-schedulers as Deadline Budget Constraint (DBC) and Workload Management System (WMS) by using the simulator GridSim to prove the improvement that offers this new algorithm.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130396098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121732
C. López-Molina, H. Bustince, E. Tartas, A. Jurio, B. Baets
The multiscale techniques for edge detection represent an effort to combine the spatial accuracy of small-scale methods with the ability to deal with spurious responses inherent to the large scale ones. In this work we introduce a multiscale extension of the Sobel method for edge detection based on Gaussian smoothing and fine-to-coarse edge tracking. We include examples illustrating the procedure and its results, as well as some quantitative measurements of the improvement obtained with the multiscale approach with respect to the original one.
{"title":"Multiscale edge detection based on the Sobel method","authors":"C. López-Molina, H. Bustince, E. Tartas, A. Jurio, B. Baets","doi":"10.1109/ISDA.2011.6121732","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121732","url":null,"abstract":"The multiscale techniques for edge detection represent an effort to combine the spatial accuracy of small-scale methods with the ability to deal with spurious responses inherent to the large scale ones. In this work we introduce a multiscale extension of the Sobel method for edge detection based on Gaussian smoothing and fine-to-coarse edge tracking. We include examples illustrating the procedure and its results, as well as some quantitative measurements of the improvement obtained with the multiscale approach with respect to the original one.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127679147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121847
M. E. Cintra, C. A. A. Meira, M. C. Monard, H. Camargo, L. Rodrigues
This paper proposes the use of fuzzy decision trees for coffee rust warning, the most economically important coffee disease in the world. The models were induced using field data collected during 8 years. Using different subsets of attributes from the original data, three distinct datasets were constructed. The class attribute, representing the monthly infection rate, was used to construct six datasets according to two distinct infection rates. Induced models can be used to trigger alerts when estimated monthly disease infection rates reach one of the two thresholds. The first threshold allows applying preventive actions, whereas the second one requires a curative action. The fuzzy decision tree models were compared to the ones induced by a classic decision tree algorithm, taking into account the accuracy and the syntactic complexity of the models, as well as its quality according to an expert opinion. The fuzzy models showed better accuracy power and interpretability.
{"title":"The use of fuzzy decision trees for coffee rust warning in Brazilian crops","authors":"M. E. Cintra, C. A. A. Meira, M. C. Monard, H. Camargo, L. Rodrigues","doi":"10.1109/ISDA.2011.6121847","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121847","url":null,"abstract":"This paper proposes the use of fuzzy decision trees for coffee rust warning, the most economically important coffee disease in the world. The models were induced using field data collected during 8 years. Using different subsets of attributes from the original data, three distinct datasets were constructed. The class attribute, representing the monthly infection rate, was used to construct six datasets according to two distinct infection rates. Induced models can be used to trigger alerts when estimated monthly disease infection rates reach one of the two thresholds. The first threshold allows applying preventive actions, whereas the second one requires a curative action. The fuzzy decision tree models were compared to the ones induced by a classic decision tree algorithm, taking into account the accuracy and the syntactic complexity of the models, as well as its quality according to an expert opinion. The fuzzy models showed better accuracy power and interpretability.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"573 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127068784","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121736
Néstor Morales, J. Toledo, L. Acosta
In this paper, a new method for the automated video surveillance of wide areas is described. Using the images obtained from a set of cameras installed on an autonomous vehicle, a video surveillance tool has been developed, based on the comparison between images that have been taken in the same place but at different times. The vehicle drives around the watched area, looking for intruders. The method described in this paper is the image comparison system used for this task, and it is based on image registration and change detection techniques. The system has been fully tested, obtaining promising results. The validation process shows the good performance of the methods selected to develop the application. It is also able to be executed in real time with good detection rates.
{"title":"Object detection in non-stationary video surveillance for an autonomous vehicle","authors":"Néstor Morales, J. Toledo, L. Acosta","doi":"10.1109/ISDA.2011.6121736","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121736","url":null,"abstract":"In this paper, a new method for the automated video surveillance of wide areas is described. Using the images obtained from a set of cameras installed on an autonomous vehicle, a video surveillance tool has been developed, based on the comparison between images that have been taken in the same place but at different times. The vehicle drives around the watched area, looking for intruders. The method described in this paper is the image comparison system used for this task, and it is based on image registration and change detection techniques. The system has been fully tested, obtaining promising results. The validation process shows the good performance of the methods selected to develop the application. It is also able to be executed in real time with good detection rates.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123459887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121849
Javier Pérez-Rodríguez, N. García-Pedrajas
Gene structure prediction consists of determining which parts of a genomic sequence of the cell are coding, and constructing the whole gene from its start site to its stop codon. Gene recognition is one of the most important open problems in bioinformatics. The subtle sources of evidence and the many pitfalls of the problem make gene recognition in eukaryotes one of the most challenging tasks in this field. Gene recognition may be considered as a search problem, where many evidence sources are combined in a scoring function that must be maximized to obtain the structure of a probable gene. Using an intrinsic method, we propose a combination of evolutionary computation and support vector machines for gene structure prediction. Specifically, we use support vector machines (SVMs) to localize and score the functional sites along the genomic sequence, reducing the search space. Evolutionary computation is used to evolve a population where the individuals are correct gene structures. The flexibility of evolutionary computation can be used to account for the complexities of the problem, which are growing as our knowledge of the molecular processes of transcription and translation deepens. Our results show that with a very simple program we are able to achieve very good accuracies in the recognition of genes in human chromosome 19.
{"title":"Evolutionary computation, combined with support vector machines, for gene structure prediction","authors":"Javier Pérez-Rodríguez, N. García-Pedrajas","doi":"10.1109/ISDA.2011.6121849","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121849","url":null,"abstract":"Gene structure prediction consists of determining which parts of a genomic sequence of the cell are coding, and constructing the whole gene from its start site to its stop codon. Gene recognition is one of the most important open problems in bioinformatics. The subtle sources of evidence and the many pitfalls of the problem make gene recognition in eukaryotes one of the most challenging tasks in this field. Gene recognition may be considered as a search problem, where many evidence sources are combined in a scoring function that must be maximized to obtain the structure of a probable gene. Using an intrinsic method, we propose a combination of evolutionary computation and support vector machines for gene structure prediction. Specifically, we use support vector machines (SVMs) to localize and score the functional sites along the genomic sequence, reducing the search space. Evolutionary computation is used to evolve a population where the individuals are correct gene structures. The flexibility of evolutionary computation can be used to account for the complexities of the problem, which are growing as our knowledge of the molecular processes of transcription and translation deepens. Our results show that with a very simple program we are able to achieve very good accuracies in the recognition of genes in human chromosome 19.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127824400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2011-11-01DOI: 10.1109/ISDA.2011.6121754
E. Zarrazola, D. Gómez, J. Montero, J. Yáñez, A. I. G. D. Castro
In this paper we propose an efficient and polynomial hierarchical clustering technique for unsupervised classification of items being connected by a graph. The output of this algorithm shows the cluster evolution in a divisive way, in such a way that as soon as two items are included in the same cluster they will join a common cluster until the last iteration, in which all the items belong to a singleton cluster. This output can be viewed as a fuzzy clustering in which for each alpha cut we have a standard cluster of the network. The clustering tool we present in this paper allows a hierarchical clustering of related items avoiding some unrealistic constraints that are quite often assumed in clustering problems. The proposed procedure is applied to a hierarchical segmentation problem in astronomical images.
{"title":"Network clustering by graph coloring: An application to astronomical images","authors":"E. Zarrazola, D. Gómez, J. Montero, J. Yáñez, A. I. G. D. Castro","doi":"10.1109/ISDA.2011.6121754","DOIUrl":"https://doi.org/10.1109/ISDA.2011.6121754","url":null,"abstract":"In this paper we propose an efficient and polynomial hierarchical clustering technique for unsupervised classification of items being connected by a graph. The output of this algorithm shows the cluster evolution in a divisive way, in such a way that as soon as two items are included in the same cluster they will join a common cluster until the last iteration, in which all the items belong to a singleton cluster. This output can be viewed as a fuzzy clustering in which for each alpha cut we have a standard cluster of the network. The clustering tool we present in this paper allows a hierarchical clustering of related items avoiding some unrealistic constraints that are quite often assumed in clustering problems. The proposed procedure is applied to a hierarchical segmentation problem in astronomical images.","PeriodicalId":433207,"journal":{"name":"2011 11th International Conference on Intelligent Systems Design and Applications","volume":"7 5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129194339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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