• DocumentCode
    3182914
  • Title

    Cuckoo Search Clustering Algorithm: A novel strategy of biomimicry

  • Author

    Goel, Samiksha ; Sharma, Arpita ; Bedi, Punam

  • Author_Institution
    Dept. of Comput. Sci., Delhi Univ., Delhi, India
  • fYear
    2011
  • fDate
    11-14 Dec. 2011
  • Firstpage
    916
  • Lastpage
    921
  • Abstract
    A novel, nature inspired, unsupervised classification method, based on the most recent metaheuristic algorithm, stirred by the breeding strategy of the parasitic bird, the cuckoo, is introduced in this paper. The proposed Cuckoo Search Clustering Algorithm (CSCA) yields good results on benchmark dataset. Inspired by the results, the proposed algorithm is validated on two real time remote sensing satellite- image datasets for extraction of the water body, which itself is a quite complex problem. The CSCA makes use of Davies-Bouldin index (DBI) as fitness function. Also a method for generation of new cuckoos used in this algorithm is introduced. The resulting algorithm is conceptually simpler, takes less parameter than other nature inspired algorithms, and, after some parameter tuning, yields very good results.
  • Keywords
    geophysical image processing; optimisation; pattern classification; pattern clustering; remote sensing; search problems; visual databases; water resources; Davies-Bouldin index; biomimicry; breeding strategy; cuckoo search clustering algorithm; fitness function; metaheuristic algorithm; nature inspired classification method; parasitic bird; real time remote sensing satellite-image; unsupervised classification method; water body extraction; Accuracy; Algorithm design and analysis; Benchmark testing; Clustering algorithms; Optimization; Remote sensing; Satellites; Cuckoo Search Clustering Algorithm (CSCA); Cuckoo Serach; Davies-Bouldin index (DBI); Satellite Image;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Information and Communication Technologies (WICT), 2011 World Congress on
  • Conference_Location
    Mumbai
  • Print_ISBN
    978-1-4673-0127-5
  • Type

    conf

  • DOI
    10.1109/WICT.2011.6141370
  • Filename
    6141370