@InProceedings{PortoQuil:2019:CoNeAp,
               author = "Porto, Sandy Moreira and Quiles, Marcos G.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Universidade Federal de S{\~a}o Paulo (UNIFESP)}",
                title = "Clustering data streams: a complex network approach",
            booktitle = "Proceedings...",
                 year = "2019",
               editor = "Misra, S. and Stankova, E. and Korkhov, V. and Torre, C. and 
                         Tarantino, E. and Rocha, A. M. A. C. and Taniar, D. and Gervasi, 
                         O. and Apduhan, B. O. and Murgante, B.",
                pages = "52--65",
         organization = "International Conference on Computational Science and Its 
                         Applications, 19. (ICCSA)",
            publisher = "Springer Verlag",
             keywords = "Data streams, clustering, complex network.",
             abstract = "Clustering data streams is an interesting and challenging problem. 
                         Although several solutions have been proposed in the literature, 
                         some drawbacks remain. For instance, how to deal effectively with 
                         the offline process for partitioning the micro-clusters into 
                         macro-clusters is still an open problem. Typically, the k-means 
                         algorithm is considered in this phase, which despite precise 
                         results, require a mandatory user-defined parameter k, that 
                         defines the number of expected clusters. In this paper, we propose 
                         a new clustering method for data stream, named Prototype Networks. 
                         This method takes the complex network structure to represent the 
                         set of micro-clusters. This approach has proven to be advantageous 
                         mainly because these networks have an inherent community 
                         structure. As a consequence, the offline phase might be easily 
                         handled by a community detection algorithm, such as Infomap. The 
                         communities detected represents the cluster structure of the data 
                         assuming that the network construction was designed for this 
                         purpose. Computer experiments demonstrated the feasibility of the 
                         proposed approach. Moreover, the proposed method can detect 
                         automatically the number of clusters in evolving scenarios, which 
                         is a useful feature when dealing with data streams with concept 
                         drift.",
  conference-location = "Saint Petersburg, Russia",
      conference-year = "01-04 July",
                  doi = "10.1007/978-3-030-24289-3_5",
                  url = "http://dx.doi.org/10.1007/978-3-030-24289-3_5",
                 isbn = "978-303024288-6",
                 issn = "03029743",
             language = "en",
        urlaccessdate = "27 abr. 2024"
}