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
Link To Document