Study of Canopy and K-Means Clustering Algorithm Based on Mahout for E-commerce Product Quality Analysis

Abstract

With the rapid development of “Internet +”, mobile application and communication technology, e-commerce has increasingly become the main shopping way for consumers in China. Aiming at quality keywords, this paper designs a distributed data clustering system based on Hadoop and mahout. In order to overcome the randomness, low accuracy and many iterations of K-Means algorithm, Canopy and K-Means Clustering Algorithm based on Mahout is designed by using the advantages of Canopy algorithm, that is, no need to specify the number of clusters, high efficiency and conciseness Clustering keywords. Based on the algorithm, good results have been obtained.