Cluster analysis is a valuable tool for exploratory pattern analysis, especially when very little a priori knowledge about the data is available. Distributed systems, based on high speed intranet connections, provide new tools in order to design new and faster clustering algorithms. Here, a parallel genetic algorithm for clustering called PGAC is described. The used strategy of parallelization is the island model paradigm where different populations of chromosomes (called demes) evolve locally to each processor and from time to time some individuals are moved from one deme to another. Experiments have been performed for testing the benefits of the parallelisation paradigm in terms of computation time and correctness of the solution.
{"title":"PGAC: A Parallel Genetic Algorithm for Data Clustering","authors":"Giosuè Lo Bosco","doi":"10.1109/CAMP.2005.41","DOIUrl":"https://doi.org/10.1109/CAMP.2005.41","url":null,"abstract":"Cluster analysis is a valuable tool for exploratory pattern analysis, especially when very little a priori knowledge about the data is available. Distributed systems, based on high speed intranet connections, provide new tools in order to design new and faster clustering algorithms. Here, a parallel genetic algorithm for clustering called PGAC is described. The used strategy of parallelization is the island model paradigm where different populations of chromosomes (called demes) evolve locally to each processor and from time to time some individuals are moved from one deme to another. Experiments have been performed for testing the benefits of the parallelisation paradigm in terms of computation time and correctness of the solution.","PeriodicalId":340151,"journal":{"name":"International Workshop on Computer Architectures for Machine Perception","volume":"34 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130951216","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}
For a peer-to-peer (P2P) system holding massive amount of data, an efficient and scalable search for resource sharing is a key determinant to its practical usage. Unstructured P2P networks avoid the limitations of centralized systems and the drawbacks of a highly structured approach, because they impose few constraints on topology and data placement, and they support highly versatile search mechanisms. However their search algorithms are usually based on simple flooding schemes, showing severe inefficiencies. In this paper, to address this major limitation, we propose and evaluate the adoption of a local adaptive routing protocol. The routing algorithm adopts a simple Reinforcement Learning scheme (driven by query interactions among neighbors), in order to dynamically adapt the topology to peer interests. Preliminaries evaluations show that the approach is able to dynamically group peer nodes in clusters containing peers with shared interests and organized into a small world network.
{"title":"Reinforcement Learning for P2P Searching","authors":"L. Gatani, G. Re, A. Urso, S. Gaglio","doi":"10.1109/CAMP.2005.45","DOIUrl":"https://doi.org/10.1109/CAMP.2005.45","url":null,"abstract":"For a peer-to-peer (P2P) system holding massive amount of data, an efficient and scalable search for resource sharing is a key determinant to its practical usage. Unstructured P2P networks avoid the limitations of centralized systems and the drawbacks of a highly structured approach, because they impose few constraints on topology and data placement, and they support highly versatile search mechanisms. However their search algorithms are usually based on simple flooding schemes, showing severe inefficiencies. In this paper, to address this major limitation, we propose and evaluate the adoption of a local adaptive routing protocol. The routing algorithm adopts a simple Reinforcement Learning scheme (driven by query interactions among neighbors), in order to dynamically adapt the topology to peer interests. Preliminaries evaluations show that the approach is able to dynamically group peer nodes in clusters containing peers with shared interests and organized into a small world network.","PeriodicalId":340151,"journal":{"name":"International Workshop on Computer Architectures for Machine Perception","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2005-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132979113","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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