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    News Text Topic Clustering Optimized Method Based on TF-IDF Algorithm on Spark

    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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