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A new possibilistic-based clustering method for probability density functions and its application to detecting

Hung Tran-Nam1,2, Thao Nguyen-Trang1,2, Ha Che-Ngoc3

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Summary

This study introduces a new possibilistic clustering approach for probability density functions (PDFs) to detect abnormalities in big data. The method achieves 100% accuracy on benchmark data and high performance on image data, outperforming existing algorithms.

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Area of Science:

  • Machine Learning
  • Data Mining
  • Pattern Recognition

Background:

  • Unsupervised learning excels at abnormality detection, but existing methods primarily handle discrete data, neglecting probability density functions (PDFs).
  • Clustering PDFs is crucial for analyzing complex datasets, yet current techniques face limitations in identifying abnormal elements within these functions.

Purpose of the Study:

  • To develop a novel possibilistic clustering algorithm for identifying abnormal elements within probability density functions.
  • To enable effective abnormality detection in big data by analyzing underlying probability density functions.

Main Methods:

  • Data extraction via density functions followed by a proposed possibilistic clustering algorithm.
  • Generation of a possibilistic partition and establishment of a decision rule for abnormality recognition.
  • Comparison with baseline clustering algorithms like k-means, FCF, and Self-Updated Clustering for PDFs.

Main Results:

  • The proposed algorithm achieved 100% accuracy on simulated benchmark data, surpassing baseline methods.
  • Application to image data yielded G-mean scores from 96% to 100%, with Sensitivity (92-100%) and Specificity (100%).
  • Demonstrated superior performance in clustering PDFs and detecting abnormal elements compared to existing algorithms.

Conclusions:

  • The developed possibilistic approach effectively clusters probability density functions and detects abnormalities.
  • This method offers a robust tool for understanding internal structures within big data through PDFs.
  • The algorithm shows significant potential for research and application in the digital age for advanced data analysis.