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Research on improved k-means clustering algorithm based on Hadoop platform


Scalable and Secure Big Data I

Class Agnostic Image Common Ob

3D Reconstruction in Canonical
Abstract


In this paper, aiming at the problems of traditional Kmeans clustering algorithm in big data processing, such as performance and determination of initial clustering center, an improved k-means clustering algorithm based on Hadoop platform is proposed. This algorithm uses canopy algorithm and cosine similarity to calculate, optimizes the determination of initial clustering center by K-means algorithm, and uses parallel computing framework to expand the algorithm in parallel. To adapt to big data processing. The experimental results show that the improved k-means clustering algorithm based on Hadoop platform has better clustering effect, and also has good speedup and scalability when processing a large number of data.

KeyWords
Hadoop, Canopy Algorithm, K-means Algorithm



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