Ant-based text clustering is a promising technique that has attracted great research attention. This paper attempts to improve the standard ant-based text-clustering algorithm in two dimensions. On one hand, the ontol...Ant-based text clustering is a promising technique that has attracted great research attention. This paper attempts to improve the standard ant-based text-clustering algorithm in two dimensions. On one hand, the ontology-based semantic similarity measure is used in conjunction with the traditional vector-space-model-based measure to provide more accurate assessment of the similarity between documents. On the other, the ant behavior model is modified to pursue better algorithmic performance. Especially, the ant movement rule is adjusted so as to direct a laden ant toward a dense area of the same type of items as the ant＇s carrying item, and to direct an unladen ant toward an area that contains an item dissimilar with the surrounding items within its Moore neighborhood. Using WordNet as the base ontology for assessing the semantic similarity between documents, the proposed algorithm is tested with a sample set of documents excerpted from the Reuters-21578 corpus and the experiment results partly indicate that the proposed algorithm perform better than the standard ant-based text-clustering algorithm and the k-means algorithm.展开更多
Aimed at three basic services(event-driven,data query and stream query),the paper presents a QoS routing model for multimedia sensor networks.Moreover,based on the traditional ant-based algorithm,we propose an ant-bas...Aimed at three basic services(event-driven,data query and stream query),the paper presents a QoS routing model for multimedia sensor networks.Moreover,based on the traditional ant-based algorithm,we propose an ant-based service-aware routing(ASAR)algorithm.The ASAR chooses suitable paths to meet diverse QoS requirements from different kinds of services,thus maximizing network utilization and improving network performance.Finally,extensive simulation is conducted to verify the effectiveness of our solution and we give a detailed discussion on the effects of different system parameters.Compared to the typical routing algorithm in sensor networks and the traditional ant-based algorithm,our ASAR algorithm has better convergence and significantly provides better QoS for multiple types of services in the multimedia sensor networks.展开更多
Purpose–Swarm controlled emergence is proposed as an approach to control emergent effects in(artificial)swarms.The method involves the introduction of specific control agents into the swarm systems.Control agents beh...Purpose–Swarm controlled emergence is proposed as an approach to control emergent effects in(artificial)swarms.The method involves the introduction of specific control agents into the swarm systems.Control agents behave similar to the normal agents and do not directly influence the behavior of the normal agents.The specific design of the control agents depends on the particular swarm system considered.The aim of this paper is to apply the method to ant clustering.Ant clustering,as an emergent effect,can be observed in nature and has inspired the design of several technical systems,e.g.moving robots,and clustering algorithms.Design/methodology/approach–Different types of control agents for that ant clustering model are designed by introducing slight changes to the behavioural rules of the normal agents.The clustering behaviour of the resulting swarms is investigated by extensive simulation studies.Findings–It is shown that complex behavior can emerge in systems with two types of agents(normal agents and control agents).For a particular behavior of the control agents,an interesting swarm size dependent effect was found.The behaviour prevents clustering when the number of control agents is large,but leads to stronger clustering when the number of control agents is relatively small.Research limitations/implications–Although swarm controlled emergence is a general approach,in the experiments of this paper the authors concentrate mainly on ant clustering.It remains for future research to investigate the application of the method in other swarm systems.Swarm controlled emergence might be applied to control emergent effects in computing systems that consist of many autonomous components which make decentralized decisions based on local information.Practical implications–The particular finding,that certain behaviours of control agents can lead to stronger clustering,can help to design improved clustering algorithms by using heterogeneous swarms of agents.Originality/value–In general,the control of(unwanted)emergent effects in artificial systems is an important problem.However,to date not much research has been done on this topic.This paper proposes a new approach and opens a different research direction towards future control principles for self-organized systems that consist of a large number of autonomous components.展开更多
Inspired by the swarm intelligence in self-organizing behavior of real ant colonies, various ant-based algorithms were proposed recently for many research fields in data mining such as clustering. Compared with the pr...Inspired by the swarm intelligence in self-organizing behavior of real ant colonies, various ant-based algorithms were proposed recently for many research fields in data mining such as clustering. Compared with the previous clustering approaches such as K-means, the main advantage of ant-based clustering algorithms is that no additional information is needed, such as the initial partitioning of the data or the number of clusters. In this paper, we present an adaptive ant clustering algorithm ACAD. The algorithm uses a digraph where the vertexes represent the data to be clustered. The weighted edges represent the acceptance rate between the two data it connected. The pheromone on the edges is adaptively updated by the ants passing it. Some edges with less pheromone are progressively removed under a threshold in the process. Strong connected components of the final digraph are extracted as clusters. Experimental results on several real datasets and benchmarks indicate that ACAD is conceptually simpler, more efficient and more robust than previous research such as the classical K-means clustering algorithm and LF algorithm which.is also based on ACO展开更多
基金This work was supported in part by National Natural Science Foundation of China under Grants No.70301009 and No. 70431001, and by Ministry of Education, Culture, Sports, Science and Technology of Japan under the "Kanazawa Region, Ishikawa High-Tech Sensing Cluster of Knowledge-Based Cluster Creation Project"
文摘Ant-based text clustering is a promising technique that has attracted great research attention. This paper attempts to improve the standard ant-based text-clustering algorithm in two dimensions. On one hand, the ontology-based semantic similarity measure is used in conjunction with the traditional vector-space-model-based measure to provide more accurate assessment of the similarity between documents. On the other, the ant behavior model is modified to pursue better algorithmic performance. Especially, the ant movement rule is adjusted so as to direct a laden ant toward a dense area of the same type of items as the ant＇s carrying item, and to direct an unladen ant toward an area that contains an item dissimilar with the surrounding items within its Moore neighborhood. Using WordNet as the base ontology for assessing the semantic similarity between documents, the proposed algorithm is tested with a sample set of documents excerpted from the Reuters-21578 corpus and the experiment results partly indicate that the proposed algorithm perform better than the standard ant-based text-clustering algorithm and the k-means algorithm.
基金This work was supported by the National High Technology Research and Development Program of China(No.2006AA01Z304)the National Natural Science Foundation of China(Grant No.90612013)the Beijing Natural Science Found(No.4062024)and the NCET of MOE,China.
文摘Aimed at three basic services(event-driven,data query and stream query),the paper presents a QoS routing model for multimedia sensor networks.Moreover,based on the traditional ant-based algorithm,we propose an ant-based service-aware routing(ASAR)algorithm.The ASAR chooses suitable paths to meet diverse QoS requirements from different kinds of services,thus maximizing network utilization and improving network performance.Finally,extensive simulation is conducted to verify the effectiveness of our solution and we give a detailed discussion on the effects of different system parameters.Compared to the typical routing algorithm in sensor networks and the traditional ant-based algorithm,our ASAR algorithm has better convergence and significantly provides better QoS for multiple types of services in the multimedia sensor networks.
文摘Purpose–Swarm controlled emergence is proposed as an approach to control emergent effects in(artificial)swarms.The method involves the introduction of specific control agents into the swarm systems.Control agents behave similar to the normal agents and do not directly influence the behavior of the normal agents.The specific design of the control agents depends on the particular swarm system considered.The aim of this paper is to apply the method to ant clustering.Ant clustering,as an emergent effect,can be observed in nature and has inspired the design of several technical systems,e.g.moving robots,and clustering algorithms.Design/methodology/approach–Different types of control agents for that ant clustering model are designed by introducing slight changes to the behavioural rules of the normal agents.The clustering behaviour of the resulting swarms is investigated by extensive simulation studies.Findings–It is shown that complex behavior can emerge in systems with two types of agents(normal agents and control agents).For a particular behavior of the control agents,an interesting swarm size dependent effect was found.The behaviour prevents clustering when the number of control agents is large,but leads to stronger clustering when the number of control agents is relatively small.Research limitations/implications–Although swarm controlled emergence is a general approach,in the experiments of this paper the authors concentrate mainly on ant clustering.It remains for future research to investigate the application of the method in other swarm systems.Swarm controlled emergence might be applied to control emergent effects in computing systems that consist of many autonomous components which make decentralized decisions based on local information.Practical implications–The particular finding,that certain behaviours of control agents can lead to stronger clustering,can help to design improved clustering algorithms by using heterogeneous swarms of agents.Originality/value–In general,the control of(unwanted)emergent effects in artificial systems is an important problem.However,to date not much research has been done on this topic.This paper proposes a new approach and opens a different research direction towards future control principles for self-organized systems that consist of a large number of autonomous components.
基金This project is supported in part by National Natural Science Foundation of China （60673060）, Science Foundation of Jiangsu Province （BK2005047）.
文摘Inspired by the swarm intelligence in self-organizing behavior of real ant colonies, various ant-based algorithms were proposed recently for many research fields in data mining such as clustering. Compared with the previous clustering approaches such as K-means, the main advantage of ant-based clustering algorithms is that no additional information is needed, such as the initial partitioning of the data or the number of clusters. In this paper, we present an adaptive ant clustering algorithm ACAD. The algorithm uses a digraph where the vertexes represent the data to be clustered. The weighted edges represent the acceptance rate between the two data it connected. The pheromone on the edges is adaptively updated by the ants passing it. Some edges with less pheromone are progressively removed under a threshold in the process. Strong connected components of the final digraph are extracted as clusters. Experimental results on several real datasets and benchmarks indicate that ACAD is conceptually simpler, more efficient and more robust than previous research such as the classical K-means clustering algorithm and LF algorithm which.is also based on ACO
