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Fusion effect of SVM in spark architecture for speech data mining in cluster structure

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Abstract

Fusion effect of SVM in the Spark architecture for speech data mining in cluster structure is studied in this manuscript. Based on the information entropy of nodes, the data in clusters are fused to eliminate redundant data and improve the efficiency of information fusion. Information entropy is a statistical form based on the characteristics of information representation, which reflects the average amount of information in information. Based on the Spark platform SVM algorithm, the frequent items with the highest support after each sort are directly recursively obtained, and the transaction data set is allocated to each computing node. The structure of the item head table directly affects the efficiency of the algorithm, so optimizing the structure of the item head table can improve the efficiency of the algorithm in constructing FP-Tree, and then improve the efficiency of the whole algorithm. The proposed speech data mining algorithm can cluster, analyze, and comprehensively detection the saliency information, the detection accuracy is much higher than the state-of-the-art models. The experimental results compared with the latest research have reflected that fact that the proposed model has the better performance and robustness.

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Authors and Affiliations

  1. Visual Arts Department, Hunan Mass Media Vocational Technical College, Changsha, Hunan, China

    Jianfei Shen

  2. GoPerception Laboratory, Ithaca, USA

    Harry Haoxiang Wang

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  1. Jianfei Shen
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  2. Harry Haoxiang Wang
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Correspondence to Jianfei Shen.

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Shen, J., Wang, H.H. Fusion effect of SVM in spark architecture for speech data mining in cluster structure. Int J Speech Technol 23, 481–488 (2020). https://doi.org/10.1007/s10772-020-09710-1

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  • DOI: https://doi.org/10.1007/s10772-020-09710-1

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