Incremental Cluster Detection using a Soft Computing Approach

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Article{Reshamwala:2010:IJCA,
  title =        "Incremental Cluster Detection using a Soft Computing
                 Approach",
  author =       "Alpa Reshamwala and Vijay Katkar and Mamta Ubnare",
  year =         "2010",
  journal =      "International Journal of Computer Applications",
  volume =       "11",
  number =       "8",
  pages =        "13--17",
  month =        dec,
  publisher =    "Foundation of Computer Science",
  keywords =     "genetic algorithms, genetic programming, data mining,
                 clustering, parallelism, density, incremental mining",
  ISSN =         "09758887",
  bibsource =    "OAI-PMH server at www.doaj.org",
  oai =          "oai:doaj-articles:07f51973178310ec1bc6ec831a50f918",
  URL =          "http://www.ijcaonline.org/volume11/number8/pxc3872155.pdf",
  size =         "5 pages",
  abstract =     "Clustering is the process of locating patterns in
                 large data sets. As databases continue to grow in size,
                 efficient and effective clustering algorithms play a
                 paramount role in data mining applications. Traditional
                 clustering approaches usually analyse static data sets
                 in which objects are kept unchanged after being
                 processed, but many practical datasets are dynamically
                 modified which means some previously learnt patterns
                 have to be updated accordingly. Re-clustering the whole
                 dataset from scratch is not a good choice due to the
                 frequent data modifications and the limited
                 out-of-service time, so the development of incremental
                 clustering approaches is highly desirable. In this
                 paper, we propose an incremental algorithm, IPYRAMID:
                 Incremental Parallel hYbrid clusteRing using genetic
                 progrAmming and Multiobjective fItness with Density
                 employs a combination of data parallelism, genetic
                 programming (GP), special operators, and
                 multi-objective density-based incremental fitness
                 function. Although many incremental clustering
                 algorithms have been proposed which can handle
                 insertion of new record properly using incremental
                 approach but cannot handle deletion of record properly.
                 This issue is resolved in the proposed algorithm and
                 density based incremental fitness function that helps
                 to handle outliers. Use of parallelism increases the
                 speed of execution as well as identifies clusters of
                 arbitrary shapes. The incremental merge engine can
                 dynamically determine the number of clusters.
                 Preliminary experimental results show that it can
                 increase the efficiency of clustering process.",
}

Genetic Programming entries for Alpa Reshamwala Vijay Katkar Mamta Ubnare

Citations