Zhuo Zhou1, Jiaohua Qin1,*, Xuyu Xiang1, Yun Tan1, Qiang Liu1, Neal N. Xiong2
CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 217-231, 2020, DOI:10.32604/cmc.2020.06431
Abstract Due to the slow processing speed of text topic clustering in stand-alone
architecture under the background of big data, this paper takes news text as the research
object and proposes LDA text topic clustering algorithm based on Spark big data
platform. Since the TF-IDF (term frequency-inverse document frequency) algorithm
under Spark is irreversible to word mapping, the mapped words indexes cannot be traced
back to the original words. In this paper, an optimized method is proposed that TF-IDF
under Spark to ensure the text words can be restored. Firstly, the text feature is extracted
by the TF-IDF algorithm combined CountVectorizer… More >
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