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Clustering using Cuckoo search levy flight

机译:使用Cuckoo搜索征税航班进行聚类

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摘要

Clustering of Web document has become a vital task, due to the tremendous amount of information that is available on web today. The task of finding suitable information with less time has become a big challenge in information retrieval. So, it's very much necessary to adopt a method that can be used organize the information well. This is possible only when good document groups are formed, which in turn can be achieved when effective and optimized cluster heads are identified. Our concern is to apply an algorithm for web document clustering. The algorithm proposed in this paper is, Cuckoo Search based on Levy Flight. Efficient cluster heads can be located using proposed Cuckoo Search algorithm. And Levy Flight helps us to speed up the local search which also ensures that it covers output domain efficiently. This algorithm is simple, efficient and it is easy to implement. A relative study of the proposed Cuckoo Search based on Levy Flight and K-means algorithm is carried out. The obtained result shows that good performance can be achieved when Cuckoo Search based on Levy Flight algorithm is used for clustering of web documents.
机译:Web文档的群集已成为一项至关重要的任务,这是由于当今Web上可用的信息量巨大。在更少的时间内找到合适信息的任务已成为信息检索中的一大挑战。因此,非常有必要采用一种可以很好地组织信息的方法。仅当形成良好的文档组时才有可能,而这又可以在确定有效且优化的簇头时实现。我们关心的是将一种算法应用于Web文档聚类。本文提出的算法是基于Levy Flight的杜鹃搜索。使用建议的布谷鸟搜索算法可以找到有效的簇头。 Levy Flight帮助我们加快了本地搜索的速度,这也确保了它有效地覆盖了输出域。该算法简单,高效且易于实现。对基于Levy Flight和K-means算法的布谷鸟搜索进行了相关研究。所得结果表明,将基于Levy Flight算法的Cuckoo搜索用于Web文档聚类可以达到良好的性能。

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