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Research and Application of Clustering Algorithm for Text Big Data.

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This study enhances the K-means clustering algorithm for big text data analysis, improving efficiency and accuracy. Mean shift clustering is also explored as an alternative for text big data processing.

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Area of Science:

  • Data Science
  • Artificial Intelligence
  • Natural Language Processing

Background:

  • Massive text data is crucial across various fields, including finance, marketing, and medicine.
  • Domain-specific text data presents challenges due to unique vocabulary, language patterns, and noise.
  • Traditional clustering algorithms like K-means struggle with large-scale, noisy, and domain-specific text data.

Purpose of the Study:

  • To address the limitations of traditional K-means clustering for big text data.
  • To improve the efficiency and accuracy of clustering algorithms for large-scale Chinese text datasets.
  • To explore alternative clustering methods like Mean Shift for text big data analysis.

Main Methods:

  • Analysis and extraction of features from text big data.
  • Experimental evaluation of traditional K-means clustering on large datasets.
  • Modification and improvement of the K-means algorithm for enhanced performance.
  • Implementation and comparison with Mean Shift clustering for text data.

Main Results:

  • Traditional K-means exhibits low efficiency and reduced accuracy on large-scale text datasets.
  • K-means is susceptible to initial center selection and outliers, impacting results.
  • The improved K-means algorithm demonstrates enhanced execution efficiency and accuracy for large data volumes.
  • Mean Shift clustering is identified as a viable kernel density estimation-based approach for text big data.

Conclusions:

  • The enhanced K-means algorithm offers a more robust solution for clustering large text datasets.
  • Mean Shift clustering provides an effective alternative, leveraging density estimation for text data analysis.
  • Optimized clustering algorithms are essential for extracting valuable insights from the growing volume of text big data.