NDLI: Cuckoo Search Clustering Algorithm: A novel strategy of biomimicry

Content Provider	IEEE Xplore Digital Library
Author	Goel, S. Sharma, A. Bedi, P.
Copyright Year	2011
Description	Author affiliation: Department of Computer Science, Delhi University, India (Goel, S.; Sharma, A.; Bedi, P.)
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.
Starting Page	916
Ending Page	921
File Size	727981
Page Count	6
File Format	PDF
ISBN	9781467301275
e-ISBN	9781467301268
DOI	10.1109/WICT.2011.6141370
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-12-11
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis Satellites Accuracy Clustering algorithms Cuckoo Serach Cuckoo Search Clustering Algorithm (CSCA) Benchmark testing Satellite Image Davies-Bouldin index (DBI) Optimization Remote sensing
Content Type	Text
Resource Type	Article

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